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Motion Customization Of Text To Video Diffusion Models Papers

0 papers in the Motion Customization Of Text To Video Diffusion Models research area.

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Motion Customization Of Text To Video Diffusion Models: Research Area — Prolific Citations Library

Discover 1 peer-reviewed study in Motion Customization Of Text To Video Diffusion Models (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 1 peer-reviewed paper in the research area of Motion Customization Of Text To Video Diffusion Models in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching

Authors: Y Wu, C Huang, F Yang, F Wang

Year: 2025

Published in: ArXiv

Institution: Nvidia, National Taiwan University

Research Area: Motion Customization of Text-to-Video Diffusion Models

Discipline: Computer Vision, Pattern Recognition

MotionMatcher is a novel framework for motion customization in text-to-video (T2V) diffusion models, using high-level spatio-temporal motion features rather than pixel-level objectives, achieving state-of-the-art performance.

Methods: Fine-tuning pre-trained text-to-video diffusion models at feature level by comparing spatio-temporal motion features instead of pixel-level objectives to address motion customization from reference videos.

Key Findings: Efficacy of motion customization in T2V models; ability to accurately capture complex motion and avoid content leakage from reference videos.