Xiaohang Yang

Qifan Fu, Xiaohang Yang, Muhammad Asad et al.

Diffusion models have shown their remarkable ability to synthesize images, including the generation of humans in specific poses. However, current models face challenges in adequately expressing conditional control for detailed hand pose generation, leading to significant distortion in the hand regions. To tackle this problem, we first curate the How2Sign dataset to provide richer and more accurate hand pose annotations. In addition, we introduce adaptive, multi-modal fusion to integrate characters' physical features expressed in different modalities such as skeleton, depth, and surface normal. Furthermore, we propose a novel Region-Aware Cycle Loss (RACL) that enables the diffusion model training to focus on improving the hand region, resulting in improved quality of generated hand gestures. More specifically, the proposed RACL computes a weighted keypoint distance between the full-body pose keypoints from the generated image and the ground truth, to generate higher-quality hand poses while balancing overall pose accuracy. Moreover, we use two hand region metrics, named hand-PSNR and hand-Distance for hand pose generation evaluations. Our experimental evaluations demonstrate the effectiveness of our proposed approach in improving the quality of digital human pose generation using diffusion models, especially the quality of the hand region. The source code is available at https://github.com/fuqifan/Region-Aware-Cycle-Loss.

Xiaohang Yang, Qing Wang, Jiahao Yang et al.

Motion retargeting seeks to faithfully replicate the spatio-temporal motion characteristics of a source character onto a target character with a different body shape. Apart from motion semantics preservation, ensuring geometric plausibility and maintaining temporal consistency are also crucial for effective motion retargeting. However, many existing methods prioritize either geometric plausibility or temporal consistency. Neglecting geometric plausibility results in interpenetration while neglecting temporal consistency leads to motion jitter. In this paper, we propose a novel sequence-to-sequence model for seamless Spatial-Temporal aware motion Retargeting (STaR), with penetration and consistency constraints. STaR consists of two modules: (1) a spatial module that incorporates dense shape representation and a novel limb penetration constraint to ensure geometric plausibility while preserving motion semantics, and (2) a temporal module that utilizes a temporal transformer and a novel temporal consistency constraint to predict the entire motion sequence at once while enforcing multi-level trajectory smoothness. The seamless combination of the two modules helps us achieve a good balance between the semantic, geometric, and temporal targets. Extensive experiments on the Mixamo and ScanRet datasets demonstrate that our method produces plausible and coherent motions while significantly reducing interpenetration rates compared with other approaches. Code page: https://github.com/XiaohangYang829/STaR.