Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work 2.1. Motion Reconstruction from Sparse Input 2.2. Human Motion Generation SAGE: Stratified Avatar Generation and 3.1. Problem Statement and Notation 3.2. Disentangled Motion Representation 3.3. Stratified Motion Diffusion 3.4. Implementation Details Experiments and Evaluation Metrics 4.1. Dataset and Evaluation Metrics 4.2. Quantitative and Qualitative Results 4.3. Ablation Study Conclusion and References Related Work 2.1. Motion Reconstruction from Sparse Input 2.2. Human Motion Generation Related Work 2.1. Motion Reconstruction from Sparse Input 2.1. Motion Reconstruction from Sparse Input 2.2. Human Motion Generation 2.2. Human Motion Generation SAGE: Stratified Avatar Generation and 3.1. Problem Statement and Notation 3.2. Disentangled Motion Representation 3.3. Stratified Motion Diffusion 3.4. Implementation Details SAGE: Stratified Avatar Generation and 3.1. Problem Statement and Notation SAGE: Stratified Avatar Generation and 3.1. Problem Statement and Notation 3.2. Disentangled Motion Representation 3.2. Disentangled Motion Representation 3.3. Stratified Motion Diffusion 3.3. Stratified Motion Diffusion 3.4. Implementation Details 3.4. Implementation Details Experiments and Evaluation Metrics 4.1. Dataset and Evaluation Metrics 4.2. Quantitative and Qualitative Results 4.3. Ablation Study Experiments and Evaluation Metrics 4.1. Dataset and Evaluation Metrics 4.1. Dataset and Evaluation Metrics 4.2. Quantitative and Qualitative Results 4.2. Quantitative and Qualitative Results 4.3. Ablation Study 4.3. Ablation Study Conclusion and References Conclusion and References Conclusion and References Supplementary Material Supplementary Material A. Extra Ablation Studies A. Extra Ablation Studies B. Implementation Details B. Implementation Details 3.4. Implementation Details For the inference stage, we evaluate our model in an online manner. Specifically, we fix the sequence length at 20 for both the input and the output of our model, and only the last pose in the output motion sequence is retained. Given a sparse observation sequence, we apply our model using a sliding window approach. For the first 20 poses in the motion sequence, we predict by padding the sparse observation sequence x at the beginning with the first available observation. We make this choice considering the practicality and relevance of online inference in real-world application scenarios. This allows the motion sequences to be predicted in a frame-by-frame manner. In addition, we employ a simple two-layer GRU [9] on the top of the full body decoder as a temporal memory to smooth the prediction of the output sequence with minimal computational expense, and we term it as a Refiner. To train this Refiner, we use the same velocity loss as [54]. Our model takes 0.74ms to infer 1 frame on a single NVIDIA RTX3090 GPU. Authors: (1) Han Feng, equal contributions, ordered by alphabet from Wuhan University; (2) Wenchao Ma, equal contributions, ordered by alphabet from Pennsylvania State University; (3) Quankai Gao, University of Southern California; (4) Xianwei Zheng, Wuhan University; (5) Nan Xue, Ant Group (xuenan@ieee.org); (6) Huijuan Xu, Pennsylvania State University. Authors: Authors: (1) Han Feng, equal contributions, ordered by alphabet from Wuhan University; (2) Wenchao Ma, equal contributions, ordered by alphabet from Pennsylvania State University; (3) Quankai Gao, University of Southern California; (4) Xianwei Zheng, Wuhan University; (5) Nan Xue, Ant Group (xuenan@ieee.org); (6) Huijuan Xu, Pennsylvania State University. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv
