DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models

• URL: http://arxiv.org/abs/2512.22324v1
• Date: Fri, 26 Dec 2025 15:06:09 GMT
• Title: DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models
• Authors: Jianrong Zhang, Hehe Fan, Yi Yang,
• Abstract summary: We propose DeMoGen, a compositional training paradigm for decompositional learning.<n>We introduce three training variants to encourage a decompositional understanding of motion.<n>We also demonstrate that the decomposed motion concepts can be flexibly recombined to generate diverse and novel motions.
• Score: 32.501737272601225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motions are compositional: complex behaviors can be described as combinations of simpler primitives. However, existing approaches primarily focus on forward modeling, e.g., learning holistic mappings from text to motion or composing a complex motion from a set of motion concepts. In this paper, we consider the inverse perspective: decomposing a holistic motion into semantically meaningful sub-components. We propose DeMoGen, a compositional training paradigm for decompositional learning that employs an energy-based diffusion model. This energy formulation directly captures the composed distribution of multiple motion concepts, enabling the model to discover them without relying on ground-truth motions for individual concepts. Within this paradigm, we introduce three training variants to encourage a decompositional understanding of motion: 1. DeMoGen-Exp explicitly trains on decomposed text prompts; 2. DeMoGen-OSS performs orthogonal self-supervised decomposition; 3. DeMoGen-SC enforces semantic consistency between original and decomposed text embeddings. These variants enable our approach to disentangle reusable motion primitives from complex motion sequences. We also demonstrate that the decomposed motion concepts can be flexibly recombined to generate diverse and novel motions, generalizing beyond the training distribution. Additionally, we construct a text-decomposed dataset to support compositional training, serving as an extended resource to facilitate text-to-motion generation and motion composition.

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