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Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References III. VIRTUAL AUDIENCE FRAMEWORK In this section, we present the proposed virtual audience framework. We focus on online events where a single client provides the content for all other participants in the session. This single presenting client interacts with the audience based on their reactions and uses this as feedback for their future behavior. Such feedback is especially relevant for performing artists that rely on live shows. A. Concept In the scenario we focus on, the presenter depends on the reaction of the entire audience rather than detailed feedback by individuals. In case of multiple participants talking or the entire audience sending feedback at the same time, noise can accumulate and drown out valuable feedback. To avoid accumulating noise, the audio of multiple participants in an online meeting typically needs to be synchronized. Since nonverbal acoustic feedback information such as clapping, whistling, booing, and laughter is less complex than speech, we propose to avoid requiring complex synchronization schemes. We only require an abstract representation of the current audience state denoting which participant is actively clapping, whistling, booing, and laughter. This abstract audience state can then be shared with all participants. The acoustic feedback can then be synthesized locally from the received audience state. This way, each participant can be played the overall acoustic feedback without having to synchronize raw audio. B. Implementation To implement the proposed concept, we share the abstract audience state information with all participants via a central server. We define the audience state as a vector of binary variables for each participant, with each variable representing whether the user is exhibiting a particular reaction. Whenever a client changes their reaction state, the updated state information is sent to the central virtual audience server. The central virtual audience server merges all information and broadcasts the updated current audience state information to every client. Finally, every client synthesises the audience feedback locally for the current audience state. The transmission of such abstract state information results in an additional transmission rate of a few bytes which is fundamentally less compared to the transmission of an audio or video stream. Figure 2 summarizes the concept for the virtual audience framework. On the client side, we define an input interface that collects the abstract state information from the users and an output interface that converts the current audience state information into an audio stream. For synthesizing the audio, any state-ofthe-art technique such as AudioGen [8] or MusicGen [26] can be used. The input interface enables a flexible implementation due to the simplicity of the abstract state information that will be transmitted to the virtual audience server. A Graphical User Interface (GUI) is a simple way for allowing audience contributions. Similar to hand waving in Zoom, participants can share feedback via their GUI. This feedback is collected on the sever and the resulting audience state will be synthesized on every client into an acoustic audience signal. We obtain the abstract state information from the clapping, whistling, booing, and laughter of the user using a GUI or detection methods such as [34]. The transmission of the abstract state information instead of the actual audio signal avoids audio synchronization issues and comes with a negligible transmission overhead as small as a few bytes. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References III. VIRTUAL AUDIENCE FRAMEWORK In this section, we present the proposed virtual audience framework. We focus on online events where a single client provides the content for all other participants in the session. This single presenting client interacts with the audience based on their reactions and uses this as feedback for their future behavior. Such feedback is especially relevant for performing artists that rely on live shows. A. Concept A. Concept In the scenario we focus on, the presenter depends on the reaction of the entire audience rather than detailed feedback by individuals. In case of multiple participants talking or the entire audience sending feedback at the same time, noise can accumulate and drown out valuable feedback. To avoid accumulating noise, the audio of multiple participants in an online meeting typically needs to be synchronized. Since nonverbal acoustic feedback information such as clapping, whistling, booing, and laughter is less complex than speech, we propose to avoid requiring complex synchronization schemes. We only require an abstract representation of the current audience state denoting which participant is actively clapping, whistling, booing, and laughter. This abstract audience state can then be shared with all participants. The acoustic feedback can then be synthesized locally from the received audience state. This way, each participant can be played the overall acoustic feedback without having to synchronize raw audio. B. Implementation B. Implementation To implement the proposed concept, we share the abstract audience state information with all participants via a central server. We define the audience state as a vector of binary variables for each participant, with each variable representing whether the user is exhibiting a particular reaction. Whenever a client changes their reaction state, the updated state information is sent to the central virtual audience server. The central virtual audience server merges all information and broadcasts the updated current audience state information to every client. Finally, every client synthesises the audience feedback locally for the current audience state. The transmission of such abstract state information results in an additional transmission rate of a few bytes which is fundamentally less compared to the transmission of an audio or video stream. Figure 2 summarizes the concept for the virtual audience framework. On the client side, we define an input interface that collects the abstract state information from the users and an output interface that converts the current audience state information into an audio stream. For synthesizing the audio, any state-ofthe-art technique such as AudioGen [8] or MusicGen [26] can be used. The input interface enables a flexible implementation due to the simplicity of the abstract state information that will be transmitted to the virtual audience server. A Graphical User Interface (GUI) is a simple way for allowing audience contributions. Similar to hand waving in Zoom, participants can share feedback via their GUI. This feedback is collected on the sever and the resulting audience state will be synthesized on every client into an acoustic audience signal. We obtain the abstract state information from the clapping, whistling, booing, and laughter of the user using a GUI or detection methods such as [34]. The transmission of the abstract state information instead of the actual audio signal avoids audio synchronization issues and comes with a negligible transmission overhead as small as a few bytes. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Understanding the Virtual Audience Framework

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Related Works on Acoustic Audience Feedback in Large Virtual Events

Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References Abstract The COVID-19 pandemic shifted many events in our daily lives into the virtual domain. While virtual conference systems provide an alternative to physical meetings, larger events require a muted audience to avoid an accumulation of background noise and distorted audio. However, performing artists strongly rely on the feedback of their audience. We propose a concept for a virtual audience framework which supports all participants with the ambience of a real audience. Audience feedback is collected locally, allowing users to express enthusiasm or discontent by selecting means such as clapping, whistling, booing, and laughter. This feedback is sent as abstract information to a virtual audience server. We broadcast the combined virtual audience feedback information to all participants, which can be synthesized as a single acoustic feedback by the client. The synthesis can be done by turning the collective audience feedback into a prompt that is fed to state-of-the-art models such as AudioGen. This way, each user hears a single acoustic feedback sound of the entire virtual event, without requiring to unmute or risk hearing distorted, unsynchronized feedback. Index Terms—Audio Synthesis, Multimedia Conference System, Virtual Audience I. INTRODUCTION Since COVID-19 first hit, many live performances moved to the virtual domain in addition to in-person events. While initially a necessary subpar substitute, virtual events are now a unique new opportunity for performing artists such as comedians, actors, and musicians. However, performing such events without instantaneous feedback is challenging and not comparable with on-site shows. The audience in larger online meeting needs to be muted to avoid disturbing background noise. Interactive audience feedback such as applause or laughter is therefore not possible. So far, virtual conference systems do not offer the functionality to share acoustic audience feedback across the session. Feedback by multiple participants at once is currently restricted to using text chat or signs such as hand waving. This lack of audience interaction poses a significant challenge for performing artists who report that Zoom shows cannot replace in-person performances [1]. It has been shown that integrating laughter into virtual meetings significantly improves the social experience of the participants [2]. Without collective feedback of the audience such as laughter, a core part of human interaction in large events is missing. Integrating acoustic audience feedback into an online conference faces several challenges. Due to network lag, different feedback sounds need to be synchronized and normalized before playing them. Raw audio data from many participants containing feedback such as laughter contains accumulating background noise. An alternative to transmitting raw audio data is to synthesize it. While the field of audio synthesis is mostly focused on speech synthesis [3], [4], [5], nonverbal audio sounds such as laughter can be synthesized as well [6], [7]. More recently, Meta’s Audiocraft [8] further increased the capabilities of audio generation based on abstract information in the form of text prompts. In this paper, we propose to leverage recent advances in audio synthesis of acoustic audience feedback to integrate collective audience sound into virtual events. In contrast to speech, information about the audience feedback such as laughter or clapping can be compressed efficiently into abstract state information. We propose a virtual audience framework that allows audience interaction without the transmission of the actual audio information. The concept of the proposed framework is shown in Figure 1. On the client input side, every participant shares abstract information about their reactions which we merge at a central virtual audience server. We then use the abstract feedback state to synthesize a single joint audience sound, for example by turning the feedback state into a prompt and using text-tosound generative models to obtain the audio. The joint audio sound is then sent back to each user, allowing each user to hear the feedback of the entire audience without overlays or distortion. By continuously updating the feedback state, the audio played for each user can be updated repeatedly to enable an audience sound that fits the current events. The rest of this paper is organized as follows. Section II summarizes related work in the field of audience audio synthesis. We propose a joint acoustic audience framework for large virtual events in Section III. Then, we present a potential implementation of synthesizing joint audio sounds using stateof-the-art generative AI. Section IV concludes the paper. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References Abstract The COVID-19 pandemic shifted many events in our daily lives into the virtual domain. While virtual conference systems provide an alternative to physical meetings, larger events require a muted audience to avoid an accumulation of background noise and distorted audio. However, performing artists strongly rely on the feedback of their audience. We propose a concept for a virtual audience framework which supports all participants with the ambience of a real audience. Audience feedback is collected locally, allowing users to express enthusiasm or discontent by selecting means such as clapping, whistling, booing, and laughter. This feedback is sent as abstract information to a virtual audience server. We broadcast the combined virtual audience feedback information to all participants, which can be synthesized as a single acoustic feedback by the client. The synthesis can be done by turning the collective audience feedback into a prompt that is fed to state-of-the-art models such as AudioGen. This way, each user hears a single acoustic feedback sound of the entire virtual event, without requiring to unmute or risk hearing distorted, unsynchronized feedback. Index Terms —Audio Synthesis, Multimedia Conference System, Virtual Audience Index Terms I. INTRODUCTION Since COVID-19 first hit, many live performances moved to the virtual domain in addition to in-person events. While initially a necessary subpar substitute, virtual events are now a unique new opportunity for performing artists such as comedians, actors, and musicians. However, performing such events without instantaneous feedback is challenging and not comparable with on-site shows. The audience in larger online meeting needs to be muted to avoid disturbing background noise. Interactive audience feedback such as applause or laughter is therefore not possible. So far, virtual conference systems do not offer the functionality to share acoustic audience feedback across the session. Feedback by multiple participants at once is currently restricted to using text chat or signs such as hand waving. This lack of audience interaction poses a significant challenge for performing artists who report that Zoom shows cannot replace in-person performances [1]. It has been shown that integrating laughter into virtual meetings significantly improves the social experience of the participants [2]. Without collective feedback of the audience such as laughter, a core part of human interaction in large events is missing. Integrating acoustic audience feedback into an online conference faces several challenges. Due to network lag, different feedback sounds need to be synchronized and normalized before playing them. Raw audio data from many participants containing feedback such as laughter contains accumulating background noise. An alternative to transmitting raw audio data is to synthesize it. While the field of audio synthesis is mostly focused on speech synthesis [3], [4], [5], nonverbal audio sounds such as laughter can be synthesized as well [6], [7]. More recently, Meta’s Audiocraft [8] further increased the capabilities of audio generation based on abstract information in the form of text prompts. In this paper, we propose to leverage recent advances in audio synthesis of acoustic audience feedback to integrate collective audience sound into virtual events. In contrast to speech, information about the audience feedback such as laughter or clapping can be compressed efficiently into abstract state information. We propose a virtual audience framework that allows audience interaction without the transmission of the actual audio information. The concept of the proposed framework is shown in Figure 1. On the client input side, every participant shares abstract information about their reactions which we merge at a central virtual audience server. We then use the abstract feedback state to synthesize a single joint audience sound, for example by turning the feedback state into a prompt and using text-tosound generative models to obtain the audio. The joint audio sound is then sent back to each user, allowing each user to hear the feedback of the entire audience without overlays or distortion. By continuously updating the feedback state, the audio played for each user can be updated repeatedly to enable an audience sound that fits the current events. The rest of this paper is organized as follows. Section II summarizes related work in the field of audience audio synthesis. We propose a joint acoustic audience framework for large virtual events in Section III. Then, we present a potential implementation of synthesizing joint audio sounds using stateof-the-art generative AI. Section IV concludes the paper. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Enabling Acoustic Audience Feedback in Large Virtual Events

-𝙷𝚘𝚠 𝚍𝚘 𝚢𝚘𝚞 𝚝𝚎𝚊𝚌𝚑 𝚊 𝚋𝚘𝚢 𝚑𝚘𝚠 𝚝𝚘 𝚍𝚛𝚘𝚠𝚗? There are mismatched socks everywhere you step; it’s like a pandemic of socks. You walk on your toes, careful not to step on any. Today is the day you learn how to swim. You are excited; it shows in your demeanor. “Oga, hurry up, na. We go leave you o.” You hear the voice of your bosom friend and classmate since the beginning of JSS1, the boy who sticks by you like glue. “No dey rush me, I dey come.” You thunder back as he sighs and tells you to hurry it up. Your room is a mess; it always is. You never remember where the ballpoint pens you so dearly love are kept; you never remember if your bag is on the top of a pile of unwashed clothes. You never remember anything, or rather, you choose to forget. “Oboi, this one goes mad o. Shey, you fit swim?” One of your friends asks, and you are tempted to roll your eyes. It is not a question you grace with a response, so you keep mute. The others come to your rescue in a flash. “No dey do like beans na, you know say we wan teach am how to swim. Sometimes this Otis dey do like person wey him brain dey glitch.” Ayo says, looking at Otis with a look of contempt. Ayo is the boy you look up to, the one most girls would kill to talk to. He looks like a cross between an American and a Nigerian, even though he keeps reminding everyone who decides to listen that he’s a full-fledged Yoruba boy. Otis is the guy everybody ignores, even you. He keeps repeating irrelevant details to fill up the silence. “No mind Otis. Na why him dey do like Otu. E no sabi anything. I go teach you how to swim, no worry.” You nod in acceptance, staring at the boy who speaks with such confidence you can’t help being shell-shocked. He’s another guy, you rever, who’s somehow good at whatever he does. Kanye is his name—the boy who does it all. It doesn’t matter what; he’s good at it. The girls used to like him until they got annoyed with him since they consider him a 𝑘𝑛𝑜𝑤-𝑖𝑡-𝑎𝑙𝑙 or rather, 𝐼𝑇𝐾. So, he sticks with the boys now. “I no mind am before na. Na so he dey do. We don know am. No worry, we get him password for hand.” You say with a laugh, as the others chorus. Four boys go to the swimming pool on a blistering hot day. Even the gods of misfortune could see nothing wrong with that idea. Or so you thought. The pool is a large one; even standing at 5’8, you can tell there are parts of the pool you absolutely must not enter. Girls are squealing like ducks out of the water, but you ignore them. It’s how girls are: noisemakers. Even in your school, it’s no different. In a bid to be seen, they often try to outshine the boys who do whatever they do with no regard for who’s watching. You do not like girls. You used to, but now you don’t. You are startled out of your reverie when Otis jostles you forward with a casual smirk on his face. He looks at you and winks while Ayo and Kanye watch you and Otis fool yourselves. The pool looks inviting as some girls leave it, clapping hands and whispering into their ears. You know how they are—gossip, all of them. You cannot remember where your hatred of women stems from, but you do know that it is from a valid place. You hear it’s called misogyny nowadays. Humans have names for everything, even inconsequential things. “So, how we go do am? Who goes in first?” Ayo asks, one of his hands raised. “Who cares? Make everybody enter jare.” Otis enters the conversation, using his body to push you aside from where you were standing. You stand at the pool's edge before you fall, your hands flailing in the air. The thought that you were just 5’8 feet tall in a pool over 7 feet deep strikes you as ironic. Nature does have a sense of humor, a wicked one. You smile, and you plunge into the water. The water cradles your head as you begin to see the beauty in the darkness of the water. The scenery changes; you lie in the arms of the one who broke you, staring into those deep brown eyes that seem to house all the pain in the universe. “I never taught you how to swim, did I?” She says, a sad smile on the edge of her lips. “You? How are you here?” You ask, fighting back tears. She looks as beautiful as you remember from the past, a phantom with no place to call home. “Does it matter? I guess they call this a place between worlds, right? So, you’re dying. How does that make you feel?” She speaks, and you close your eyes momentarily, wondering why she keeps picking you apart. “Why must you be the last thing I see before I die? Haven’t you done enough? You’re not even dead!” You scream at her face, but the only thing that comes out of your mouth are bubbles. “Dead or alive, I’m the one you love above all others. How does it feel to have your last moments with the only girl your family and friends abhor?” She grins at you, and you begin to see how messed up everything is. She isn’t your friend, she isn’t your family. She’s nothing. Yet, she’s everything. “Haven’t you done enough? Why do you keep doing this?” You say, knowing the fight has been drained out of you. “Because I can.” You can hear voices through the depths, but you refuse to heed them. You want to talk to her a little bit longer. “Wake up, dammit!” You hear a voice pulling you out of your private world with her, a voice that seems to break with pain. You don’t ask; you follow. “Thank God. Yes, that’s it. Breathe in, breathe out. Don’t worry about it. You’ll be fine, you’ll be fine…” The voice gets a little more transparent, and you open your eyes groggily to see the panic-stricken faces of Ayo and Kanye hovering over you. They fuss over you as you cough, water coming from your mouth. Your tiredness is overwhelming; your whole being begs you to go back to sleep. But in a haze, you notice that Otis is nowhere to be found. “Where is Otis?” You ask, looking around. “He’s fine. He was the one who went after you.” Ayo replies as though it is obvious. “Okay, where is he?” You ask again, and Ayo and Kanye exchange nervous glances before they try to find Otis. But you don’t say anything because you know more than anyone else. The same way you know you can never be with 𝑉𝑒𝑟𝑎, 𝑡ℎ𝑒 𝑤𝑖𝑡𝑐ℎ as most of your friends call her. You know where Otis is, and your conversation with him once rings loudly in your head. “Omo me, I just dey o. I no sabi swim. I remember when we go bar beach sef. I just dey play with sand. Me wey get that phobia for water. Wetin be the name again, sef?” Otis asks you as you hold back a laugh before replying. “Hydrophobia. The fear of water.” “Yes! Na im be that. Omo, no tell anybody sha. I wan get over am my way.” “No wahala. Nobody go hear.” You remember; you remember it all. You remember the promise you made not to tell anyone. You don’t have to look for Otis; nobody does. You know where he is. He has become one with the water. You do not remember 𝑡ℎ𝑒 𝑤𝑖𝑡𝑐ℎ anymore; everything else flies by in a haze. Did you attend Otis’ funeral? You do not remember. Your friends try to comfort you, but it is a wasted effort. You didn’t exactly love Otis; he was more like a pesky annoying fly that buzzes around and makes a fuss. You do not care about the death of a fly, yet your heart fills up so much that you fear it’s going to burst. You do not cry; crying is for babies. You stare dry-eyed at the future of the boy who never saw 16. They say the state of a person’s room reflects the state of that person’s mind. That’s why yours is squeaky clean, not a pair of socks in sight. Perhaps you do it for Otis, or maybe it’s for yourself. Maybe your room reflects your mental state, a world empty of who you once were. There are no mismatched socks anymore, just memories of the boy who learned to drown in the guise of swimming. “You can not swim. Why, then, did you do it?” You ask the empty air. But no response comes. Not one. You’re alone with the boy you taught how to drown. A fly, that’s all he was. A pesky fly. Why, then, do you cry?

Mismatched Socks

A lone boy stood, staring at the star-speckled sky, his mind drawn to the girl just a few feet from where he lay. She was a beauty, that one. He couldn’t believe she was his. Every single thing she did screamed Art. The way she linked her fingers together, the way she smiled. The one girl who knew so much about him, yet… she didn’t shy away from his questionable past. As the emotions threatened to pull him under, he shook his head to clear his thoughts and face what was before him. “A shooting star!” She screamed in excitement as she pointed at that far-off speck in the sky, becoming larger and larger by the second. “That is no shooting star.” He managed to say, fear clogging his throat. He took in the lush grass swaying in the night wind, the beauty of the stars that more or less blotted out the sky. Then, he turned to look at the girl on the ground a few feet away. She was sitting up by then, fear morphing her once beautiful features into something unspeakable. “What is that?” Her voice quaked as she asked, and he closed his eyes momentarily to stop the bile from going up his throat. “Our death.” He said, not mincing words. His throat bobbed with unspoken words and something else underlying. “I’ve always loved you. I just- I just wanted you to know. This once.” He squeezed the words out of his mouth, wrangling them like one would do to a piece of wet cloth. “You? I’ve always seen you as just a friend.” She turned towards him then, his eyes red from tears. But he was grateful for that moment. He got her to look at him finally. And her eyes shone with regret. The screams came first as the she saw earlier was big enough to blot out the stars in the sky. And the heat. God, the heat. He felt as though he was being cooked from the inside out. shooting star The sky boiled like a cauldron; giant drops of fire rained down from the clouds, turning the lush green grass into something out of Hades’s fantasy. Blades of grass crumbled before his eyes. Withering without as much as a touch from him and fizzing out of existence, flames taking its place. The greenery is overtaken by the force that darkened the universe and turned rivers into lakes of fire. She screamed the most. He didn’t. He just watched the shooting star fall and gave a small smile. She was never his to own anyway. * Darren. That was his name. The man-boy with the power to manipulate space at his whims. He was a prodigy even among his peers in Earth 75. They had hoverboards to trapeze through space. That’s right, hoverboards. Fuelled with nothing more than gravel and sand. “Gloating again? That’s not very of you.” He rolled his eyes at the voice that came from just behind him. Glenna. His childhood friend. If you can count a hundred years as childhood. Before they settled—before the metamorphosis, they never grew older than the teenage age. But, after settling for five years and being a pain in the butt for half of those he called his cadre, he realized that he was ready for metamorphosis. But then, Glenna wasn’t. Hence, he had to stop halfway and wait for her to be. ladylike “I’m not a lady, for crying out loud. This is argument number what? Five thousand and seven?” He groaned, using flawless fingers to massage his forehead. Glenna just laughed as she walked towards him. Or, more like, she glided. She had more interest in the hoverboards than he did. The room was no larger than two children’s playroom, yet it had enough space due to his meticulousness. Not a strand of space is out of place, exactly how he wanted it to be. “Number five thousand and eight. You missed one.” She laughed, planting a wet kiss on his skin. He would have gone as red as beetroot if she didn’t deftly move the conversation elsewhere. “So, I need to weave a new dress. You’ve been called a genius for so long that it has become music to my ears. I need a strand of space to weave into a cloak I’m almost done making.” She tendered her request before him, and he took in her presence. She was young, after refusing the metamorphosis for the third time. Yet.. she had eyes of peace. The brown of her irises reminded him of worlds he hadn’t dared to touch for fear of breaking them apart. He’d seen them in his wakings. They were peaceful and oh, so beautiful. The serenity in them was what drew him to them at first. “Okay, I will. But why just a strand? Why not a whole chunk?” He asked as she raised her eyebrows at him, and he almost melted into the chair he sat on. If you can call it sitting. It was a haphazard view, an upturned table, yet he was upright. The world he’d come to know extended universes and eternities. It was a world of madness, but at least it was his. “One sec.” He said, raising his hand in mock surrender. Then, everything stopped. As his concentration cleared the level of the primordial beings, he tore a portal through the world. It was a red wiggly thing struggling to close, but he held it open with sheer willpower alone. When he found out that portals were alive, he decided to lord over them one way or another. They didn’t make it easy for him, though. While it wiggled, he stretched a hand into the portal and drew a strand out of that limitless vastness, loping it through his fingers like a struggling worm. It shimmered in and out of existence, blue like the untold skies, red as fiery dragons and demons of worlds he’d only read about. “Got you.” He smiled as he held a strand. Then, the world went to hell. Before he could remove his hand from the portal, it yawned like a gaping chasm and swallowed him whole. He was screaming, but Glenna was still in timeless mode. She wouldn’t know what happened. That is mercy. That has to be. Then, the world shut itself as the portal consumed the world he’d known all of his existence and winked out of reality. The screams didn’t come. Nothing did. * Carrion. That was his name. His name. His name. He tried to hold on to it, but it slipped out of his grasp like an eel, and he followed after it, crawling after his name with it on the tip of his tongue. What was his name again? Ca.. Ca what? What was his name? The man with scars lining his body like the universe's stars was lost. He tried to think. He tried to remember where he was. Yes. He was fighting Archanoid—the mother of spiders. One could even call her a deity. He dodged an incoming web made of madness and silk, woven with anger and hate. She was large, towering above him like he was a pesky fly. He hated that. He was the warrior who battled nightmares and abominations. He had to win. But then, what was his name? His name.. what was it again? He flew out of the way as an incubator shattered where he stood one second earlier. She wasn’t playing. “Foolish mortal. Do you think you can harm me? It doesn’t matter who you are, World Walker. You can’t escape from me.” She spoke, venom dripping out of those pincer-like things. They made a clanging sound as he swallowed, looking into those red eyes that had intelligence to them, something beyond the years. “One last chance. I will give it to you. Remember, pride and ego cannot win this war. Maybe you and the others can.” And then, before he could ask what she meant.. a scary sharp finger elongated from her finger, and she him out like a fly. flicked There was screaming. And then, there was nothing. * He woke up with a groan. There were several groans across him, and he was shocked to realize that he wasn’t dead. He wasn’t scorched. But he didn’t remember what his name was. He remembered her, recognized the girl who took him as a friend before the world became a sea of fire. Why, then, was he unhurt? The others stood on shaky feet, an otherworldly feel to them. There was something he just couldn’t place. They wore things he’d never heard of, or perhaps the dark skins rippling with fire and the darkness of the deep. Maybe it was the blades of darkness in the place where teeth were meant to be, but there was something.. out of place. One of them wore nightmares on his skin; he could see death. Death rippled with muscles and scars that seemed to blot out the skin. But strangely, he didn’t feel fear. “So, an explanation is in order?” He said to no one in particular. Even the being who looked as though he was 25 and 15 at the same time. As though the world couldn’t decide on his age and let him be both instead. He noticed that this being wasn’t standing on his two feet. He was.. hovering. “We’ll see soon enough.” A boy his age spoke, and he stared at him in wonderment, wondering how alike they were. The way the boy squinted made it glaring that he used glasses to see. Same with him. Even though he didn’t even know their names, they were about ten. Some had hands for feet, and others weren’t even walking on the bare ground. They just.. existed. There was no way to explain their existence, so he didn’t bother. “An explanation.. for this. All of this.” Another boy spoke, and he didn’t balk this time. He just waited. “Here it comes.” The boy who spoke was already running. “Here, what comes? What?” He asked, but everybody else had cleared out of that barren field. The world he was in was empty. There is no sign of green. No sign of brown either. It was as though the world was drained out of colour, and no matter where he looked, despair loomed. He felt them before he saw them. He knew who they were before they spoke. And even without an introduction, he knew everything about them. The Destroyers Of Worlds. They smiled; one hovered just like that man-boy. One had skin with uncountable scars. And then, he saw himself. That cruel smile, that deadly presence. He felt a chill that had nothing to do with the weather. Someone dragged him by the scruff of his hood, and he felt.. everything. There was no other way to describe what happened. Then, he turned to stare at the other him with a cruel smile as that being let out an inferno where he was standing a moment before. It was… hot. But strangely, he wasn’t burning. He wasn’t even singed. He was.. okay. The man-boy dropped him on the ground unceremoniously, and he muttered his thanks, but the others just raised their eyebrows, staring at the beings beyond them. Beings with their faces. Beings with enough power to destroy universes. It was them. Ageless. Timeless. With a snap of a finger, they were gone like they didn’t just lay waste to a large expanse of space. “I think I know what’s going on…” A tenacious finger went up, and the squinted boy was allowed to speak. “That is us. Timeless us. And.. we destroyed our universes. Now.. we have to fight against the us that are world destroyers.” “We don’t have a choice to decline?” The man-boy asked. “We have doomed worlds. This.. is penance.” The boy spoke again, his eyes filled with trepidation and resignation. “So, we were summoned here to kill our future selves? That’s a sick sense of humor.” “This world is sick, and when the Arachnoid told me, I didn’t believe it.” The man with the scars spoke, and his scars seemed alive, melding with his body. “Can you tell us what an Arachnoid is?” Another asked. So, he told them. Everything. “Pride and ego, huh?” A guy spoke, and they finally understood what they had to do. “This is depraved. All of this. Depraved. What the hell?” He spoke, shaking with anger. “Hell doesn’t exist, apparently. Future us made sure the world became it.” A brittle laughter from the man-boy. “This is it, then? We die in a land of no name? Our names were stolen from us. I can’t dredge up a single memory.” The scarred man said, eyes smouldering. I could tell he was pissed off. “This is our penance.” The squinting boy said again, and I was tempted to throw him out of the universe. Such a goody two shoes. “I don’t care. Whatever I have to do, I will do it. And it doesn’t matter who’s on the line. I have to go back home. Also, you can’t fight fire with water. Not in my world, anyway. It takes a monster to kill another. We will take the unorthodox approach. Did they want to seclude us and destroy us? World Walkers, indeed. Bastards more likely. I won’t take that. Neither should any of you. We have the power. Can’t you feel it? We can change things.” The man-boy spoke fervently, his eyes filled with loss, anger, and something else. “That’s right. We won’t let them decide for us. If we have to die, we might as well raise hell before we do. Now, we need a name.” The hooded boy spoke, and the others nodded in approval. “How does sound?” There were hoots of approval, some going as far as clapping. Then, they stared at the sky at once. Drained of colour. But then, the World between Worlds had always been a resting place for World Walkers. The Restorer of Worlds They had to fight. And this time, they had to fight themselves. Their memories returned, and they could see themselves in the past, present, and future—the Destroyer of Worlds. No more. They decided not to make the same mistakes. Not again. This time, they’d restore all they destroyed. As the squinty-eyed kid said, it was penance.

PENANCE 

Creative animations are always attractive and can leave a professional impression. In this article, I have compiled some of my favorites to provide you with inspiration for your future projects. For each animation, I will provide a direct link, an interactive preview so you can get an initial impression on the fly as well as the author links, so you can explore more of their work, too. 1. Netflix Intro Animation By: Claudio Bonfati https://codepen.io/claudio_bonfati/pen/mdryxPv?embedable=true 2. Bongo Cat Codes By: Caroline Artz https://codepen.io/carolineartz/pen/qBOEzQa?embedable=true 3. Merry Christmas Tree By: Chris Gannon https://codepen.io/chrisgannon/pen/dypvKvR?embedable=true 4. A Long Way By: al-ro https://codepen.io/al-ro/pen/xxxxgxK?embedable=true 5. Matrix Digital Rain By: yuanchuan https://codepen.io/yuanchuan/pen/YoqWeR?embedable=true 6. Floating Heart yuanchuan https://codepen.io/yuanchuan/pen/wZJqNK?embedable=true 7. Clip Clop Clippity Clop by: Steve Gardner https://codepen.io/ste-vg/pen/oKYjKV?embedable=true 8. Neon Love By; al-ro https://codepen.io/al-ro/pen/BaaBage?embedable=true 9. Animated CSS Seasons By agathaco https://codepen.io/agathaco/pen/rpZoYd?embedable=true 10. Low Poly Animals By: Mikael Ainalem https://codepen.io/ainalem/pen/dKLpBE?embedable=true 11. Saturn Hula Hooping By: Jamie Coulter https://codepen.io/jcoulterdesign/pen/BrdPaw?embedable=true 12. Mouse-Based-Parallax Sunset By: Adam Quinlan https://codepen.io/quinlo/pen/XPVmVj?embedable=true Did You Like the Resources? Here's More 👇 Over the previous months, I've compiled The Ultimate Bundle with every resource you need, from starting to learn to code to landing your dream tech role! Includes 900+ resources in total, further divided into 99 categories for easier navigation and access! Save 1000s of hours and focus on what matters! I'm currently offering 50%OFF just for my readers! Don't miss out on this! ⭐ ! ⭐ Get it here: Ultimate Resources Bundle to Land a Job in Tech Writing has always been my passion, and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to ! my newsletter Also, connect with me on , , and ! Twitter LinkedIn GitHub Also published . here

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