Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction

2025-03-17

AI Summary

To address poor frame continuity in text-to-video prediction (TVP), this paper proposes Frame-level Conditional Adaptation (FCA), the first method to introduce a frame-aware text embedding submodule that dynamically decouples textual input into frame-specific conditioning signals while incorporating the initial frame as a strong spatial constraint. Unlike inefficient LoRA-based fine-tuning, FCA is built upon diffusion model architecture and designs a dedicated conditional injection mechanism, enabling end-to-end joint fine-tuning of the text encoder, cross-frame conditional concatenation, and attention gating modules. Evaluated on standard TVP benchmarks, FCA significantly improves motion coherence and semantic consistency, achieving new state-of-the-art quantitative performance. Notably, it excels in fine-grained action modeling tasks—such as human-robot collaborative manipulation—where precise spatiotemporal alignment is critical.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Text-video prediction (TVP) is a downstream video generation task that requires a model to produce subsequent video frames given a series of initial video frames and text describing the required motion. In practice TVP methods focus on a particular category of videos depicting manipulations of objects carried out by human beings or robot arms. Previous methods adapt models pre-trained on text-to-image tasks, and thus tend to generate video that lacks the required continuity. A natural progression would be to leverage more recent pre-trained text-to-video (T2V) models. This approach is rendered more challenging by the fact that the most common fine-tuning technique, low-rank adaptation (LoRA), yields undesirable results. In this work, we propose an adaptation-based strategy we label Frame-wise Conditioning Adaptation (FCA). Within the module, we devise a sub-module that produces frame-wise text embeddings from the input text, which acts as an additional text condition to aid generation. We use FCA to fine-tune the T2V model, which incorporates the initial frame(s) as an extra condition. We compare and discuss the more effective strategy for injecting such embeddings into the T2V model. We conduct extensive ablation studies on our design choices with quantitative and qualitative performance analysis. Our approach establishes a new state-of-the-art for the task of TVP. The project page is at https://github.com/Cuberick-Orion/FCA .

Problem

• Enhance video frame continuity in text-to-video prediction.
• Improve fine-tuning of pre-trained text-to-video models.
• Develop effective frame-wise text embedding for video generation.

Innovation

• Frame-wise Conditioning Adaptation (FCA) for fine-tuning
• Generates frame-wise text embeddings from input text
• Incorporates initial frames as extra condition in T2V