Animus3D: Text-driven 3D Animation via Motion Score Distillation
Qi Sun, Can Wang, Jiaxiang Shang, Wensen Feng, Jing Liao

TL;DR
Animus3D introduces a novel text-driven 3D animation framework that uses Motion Score Distillation with a LoRA-enhanced diffusion model and regularization techniques to produce detailed, high-fidelity animations from static 3D assets and text prompts.
Contribution
The paper proposes a new SDS alternative called Motion Score Distillation, incorporating LoRA-enhanced diffusion, regularization, and a motion refinement module for improved 3D animation quality.
Findings
Outperforms state-of-the-art baselines in motion richness and detail.
Effectively preserves appearance while animating from text prompts.
Generates high-quality, temporally consistent 3D animations.
Abstract
We present Animus3D, a text-driven 3D animation framework that generates motion field given a static 3D asset and text prompt. Previous methods mostly leverage the vanilla Score Distillation Sampling (SDS) objective to distill motion from pretrained text-to-video diffusion, leading to animations with minimal movement or noticeable jitter. To address this, our approach introduces a novel SDS alternative, Motion Score Distillation (MSD). Specifically, we introduce a LoRA-enhanced video diffusion model that defines a static source distribution rather than pure noise as in SDS, while another inversion-based noise estimation technique ensures appearance preservation when guiding motion. To further improve motion fidelity, we incorporate explicit temporal and spatial regularization terms that mitigate geometric distortions across time and space. Additionally, we propose a motion refinement…
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Taxonomy
TopicsHuman Motion and Animation · Computer Graphics and Visualization Techniques · Generative Adversarial Networks and Image Synthesis
