AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures

• URL: http://arxiv.org/abs/2608.19900v1
• Date: Thu, 20 Aug 2026 11:10:53 GMT
• Title: AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures
• Abstract summary: AvatarDynamizer is a generative method that transforms an off-the-shelf static 3D avatar into a controllable, realistic, and multi-view-consistent 4D avatar.<n>Our method effectively animates static avatars with faithful surface dynamics and outperforms competing generalizable methods in visual fidelity.
• Score: 81.30642929096783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism. Person-agnostic methods recover static 3D avatars from monocular images, videos, or text prompts, but their skeleton-driven animations lack realistic surface dynamics such as clothing wrinkles. In contrast, person-specific methods achieve high-quality rendering and realistic dynamics, but require expensive multi-view captures for each individual. Recent generalizable dynamic avatar methods struggle to embed surface dynamics, leading to either limited multi-view consistency or dynamic expressiveness. To this end, we propose AvatarDynamizer, a generative method that transforms an off-the-shelf static 3D avatar into a controllable, realistic, and multi-view-consistent 4D avatar. We introduce a novel texture-space surface-dynamics embedding and formulate avatar dynamics modeling as conditional texture generation. Our encoder--decoder representation embeds pose-dependent dynamics into dynamic texture maps, enabling compatibility with pre-trained video diffusion models while decoding them into 3D Gaussians for multi-view consistent rendering. Since existing datasets are limited in scale, sequence length, or motion diversity, we collect a large-scale multi-view dataset with long sequences covering diverse skeletal motions and surface dynamics. Experiments show that our method effectively animates static avatars with faithful surface dynamics and outperforms competing generalizable methods in visual fidelity, especially under limited dynamic training data.

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