NVIDIA's MOTIVE Beats Full Training Data Using Just 10% of Video Clips

Video generation has made enormous strides in visual quality, but temporal dynamics , the way objects actually move , remain a stubborn weak point. The core problem is surprisingly basic: nobody has had a principled way to tell which training clips are actually teaching a model how to move. NVIDIA Research's new framework, MOTIVE (MOTIon attribution for Video gEneration), tackles exactly that. Presented as an Oral and Outstanding Paper Honorable Mention at ICML 2026, it is the first framework to attribute motion , not visual appearance , in video generative models, and to use that signal to curate fine-tuning data.

The Problem Nobody Solved

Motion is the defining element of videos. Unlike image generation, which produces a single frame, video generative models must capture how objects move, interact, and obey physical constraints. Yet even with rapid progress, a fundamental question remains: which training clips influence the motion in generated videos?

Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. Prior work on data attribution , the practice of scoring training examples by how much they influence a model's outputs , was built for images. Naively applying those methods to video collapses motion into appearance, ranking clips high because they share similar backgrounds or object textures, not because they teach similar dynamics. Three specific challenges make this hard:

• Localizing motion: attribution must focus on dynamic regions, not static backgrounds
• Scaling to sequences: gradients must integrate across time, not just space
• Capturing temporal structure: velocity, acceleration, and trajectory coherence cannot be measured one frame at a time

What MOTIVE Does

MOTIVE is a scalable, motion-centric data attribution framework for video generation that identifies which training clips improve or degrade motion dynamics. The key idea is to compute influence scores , a measure borrowed from classical statistics that asks: if I upweight this training example, how much does the model's behavior change? , but to compute them in a way that only counts the motion-related part of the gradient signal.

MOTIVE isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. Once every clip in a fine-tuning dataset has a score, you can rank them, take the top 10%, and fine-tune only on that high-influence subset.