X-UniMotion: Animating Human Images with Expressive, Unified and Identity-Agnostic Motion Latents

• URL: http://arxiv.org/abs/2508.09383v1
• Date: Tue, 12 Aug 2025 22:47:20 GMT
• Title: X-UniMotion: Animating Human Images with Expressive, Unified and Identity-Agnostic Motion Latents
• Authors: Guoxian Song, Hongyi Xu, Xiaochen Zhao, You Xie, Tianpei Gu, Zenan Li, Chenxu Zhang, Linjie Luo,
• Abstract summary: We present X-UniMotion, a unified and expressive latent representation for whole-body human motion.<n>Our approach encodes multi-granular motion directly from a single image into a compact set of four disentangled latent tokens.<n>These motion latents are both highly expressive and identity-agnostic, enabling high-fidelity, detailed cross-identity motion transfer.
• Score: 17.536895865783507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present X-UniMotion, a unified and expressive implicit latent representation for whole-body human motion, encompassing facial expressions, body poses, and hand gestures. Unlike prior motion transfer methods that rely on explicit skeletal poses and heuristic cross-identity adjustments, our approach encodes multi-granular motion directly from a single image into a compact set of four disentangled latent tokens -- one for facial expression, one for body pose, and one for each hand. These motion latents are both highly expressive and identity-agnostic, enabling high-fidelity, detailed cross-identity motion transfer across subjects with diverse identities, poses, and spatial configurations. To achieve this, we introduce a self-supervised, end-to-end framework that jointly learns the motion encoder and latent representation alongside a DiT-based video generative model, trained on large-scale, diverse human motion datasets. Motion-identity disentanglement is enforced via 2D spatial and color augmentations, as well as synthetic 3D renderings of cross-identity subject pairs under shared poses. Furthermore, we guide motion token learning with auxiliary decoders that promote fine-grained, semantically aligned, and depth-aware motion embeddings. Extensive experiments show that X-UniMotion outperforms state-of-the-art methods, producing highly expressive animations with superior motion fidelity and identity preservation.

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