Impact of Random Seed and Model Robustness on High-Quality Human Image Generation
by Homology Technology FTWNovember 25th, 2024
Too Long; Didn't Read
HyperHuman’s robustness is confirmed through tests with varying random seeds, consistently producing high-quality, text-aligned images. Ethical concerns regarding misuse, such as deepfakes, are acknowledged, with a call for clear labeling of synthetic images.
Authors:
(1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.;
(2) Jian Ren, Snap Inc. with Corresponding author: [email protected];
(3) Aliaksandr Siarohin, Snap Inc.;
(4) Ivan Skorokhodov, Snap Inc.;
(5) Yanyu Li, Snap Inc.;
(6) Dahua Lin, CUHK;
(7) Xihui Liu, HKU;
(8) Ziwei Liu, NTU;
(9) Sergey Tulyakov, Snap Inc.
Table of Links
3 Our Approach and 3.1 Preliminaries and Problem Setting
3.2 Latent Structural Diffusion Model
A Appendix and A.1 Additional Quantitative Results
A.2 More Implementation Details and A.3 More Ablation Study Results
A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration
A.7 More Comparison Results and A.8 Additional Qualitative Results
A.5 IMPACT OF RANDOM SEED AND MODEL ROBUSTNESS
To further validate our model’s robustness to the impact of random seed, we inference with the same input conditions (i.e., text prompt and pose skeleton) and use different random seeds for generation. The results are shown in Fig. 5, which suggest that our proposed framework is robust to generate high-quality and text-aligned human images over multiple arbitrary random seeds.
A.6 BOARDER IMPACT AND ETHICAL CONSIDERATION
Generating realistic humans benefits a wide range of applications. It enriches creative domains such as art, design, and entertainment by enabling the creation of highly realistic and emotionally resonant visuals (Liu et al., 2022a;b). Besides, it streamlines design processes, reducing time and resources needed for tasks like graphic design and content production. However, it could be misused for malicious purposes like deepfake or forgery generation. We believe that the proper use of this technique will enhance the machine learning research and digital entertainment. We also advocate all the generated images should be labeled as “synthetic” to avoid negative social impacts.
This paper is available on arxiv under CC BY 4.0 DEED license.
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