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 B. Implementation Details B.1 Disentangled VQ-VAE B.2 Stratified Diffusion In our transformer-based model for upper-body and lowerbody diffusion, we integrate an additional DiT block as described in [29]. Each model features 12 DiT blocks, each with 8 attention heads, and an input embedding dimension of 512. The full-body decoder is structured with 6 transformer layers. B.3 Refiner The complete loss term for training the refiner can be written as: We set α, β, γ, δ to 0.01, 10, 0.05, and 0.01 to force the refiner to focus more on motion smoothness in the training process. All experiments can be carried out on a single NVIDIA GeForce RTX 3090 GPU card, using the Pytorch framework. 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
