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Xingliang Jin

3 accepted papers

2026

IntentMotion: Learning Intent-Aware Human Motion from Language in 3D Scenes

AAAI 2026technical

Generating human motion in complex 3D scenes from text is a challenging task with broad applications. However, existing methods often overlook realistic physical contact, resulting in visually plausible but physically unrealistic motion, e.g., penetration. To alleviate this, we propose IntentMotion,

Cited by 0SourcePDFScholar
2026

MoCoDiff: A Controllable Autoregressive Diffusion Model for Expressive Motion Generation

CVPR 2026

Diffusion-based motion generation has advanced rapidly, but current methods still struggle with long-horizon consistency, style control, and multi-condition guidance. A major reason is the fused-conditioning design, where semantic, stylistic, and temporal signals share a single pathway, causing inte

Cited by 0SourceScholar
2024

Arbitrary Motion Style Transfer with Multi-condition Motion Latent Diffusion Model

CVPR 2024poster

Computer animation's quest to bridge content and style has historically been a challenging venture with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality ensuring a harmonious fusion where the core narrative of t…