Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models

• URL: http://arxiv.org/abs/2501.16714v1
• Date: Tue, 28 Jan 2025 05:40:20 GMT
• Title: Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models
• Authors: Huijie Liu, Jingyun Wang, Shuai Ma, Jie Hu, Xiaoming Wei, Guoliang Kang,
• Abstract summary: Motion customization aims to adapt the diffusion model (DM) to generate videos with the motion specified by a set of video clips with the same motion concept. This paper proposes two novel strategies to enhance motion-appearance separation, including temporal attention purification (TAP) and appearance highway (AH)
• Score: 18.41701130228042
• License:
• Abstract: Motion customization aims to adapt the diffusion model (DM) to generate videos with the motion specified by a set of video clips with the same motion concept. To realize this goal, the adaptation of DM should be possible to model the specified motion concept, without compromising the ability to generate diverse appearances. Thus, the key to solving this problem lies in how to separate the motion concept from the appearance in the adaptation process of DM. Typical previous works explore different ways to represent and insert a motion concept into large-scale pretrained text-to-video diffusion models, e.g., learning a motion LoRA, using latent noise residuals, etc. While those methods can encode the motion concept, they also inevitably encode the appearance in the reference videos, resulting in weakened appearance generation capability. In this paper, we follow the typical way to learn a motion LoRA to encode the motion concept, but propose two novel strategies to enhance motion-appearance separation, including temporal attention purification (TAP) and appearance highway (AH). Specifically, we assume that in the temporal attention module, the pretrained Value embeddings are sufficient to serve as basic components needed by producing a new motion. Thus, in TAP, we choose only to reshape the temporal attention with motion LoRAs so that Value embeddings can be reorganized to produce a new motion. Further, in AH, we alter the starting point of each skip connection in U-Net from the output of each temporal attention module to the output of each spatial attention module. Extensive experiments demonstrate that compared to previous works, our method can generate videos with appearance more aligned with the text descriptions and motion more consistent with the reference videos.

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