FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control
FlexTraj introduces a point-based trajectory control framework enabling multi-granularity, alignment-agnostic image-to-video synthesis with improved efficiency.
Key Findings
Methodology
FlexTraj employs a unified point trajectory representation encoding each point with segmentation ID, trajectory ID, and optional color cues. Condition videos are generated via a pretrained VAE, capturing object identity, temporal consistency, and appearance. An efficient sequence concatenation scheme integrates these conditions into the diffusion-based video generator, utilizing LoRA fine-tuning and causal masking to enhance robustness and speed. The training adopts an annealing curriculum, gradually reducing supervision reliance from dense, aligned conditions to sparse and unaligned inputs, enabling versatile control across scenarios.
Key Results
- Quantitative evaluations show FlexTraj achieves TrajErr of 0.017 and TrajSIM of 0.908, outperforming baselines such as DAS and MagicMotion by significant margins. It maintains competitive FVD scores (~530) and high frame consistency (~0.98), demonstrating high-quality, controllable video synthesis across dense, sparse, and unaligned tasks.
- In qualitative assessments, FlexTraj effectively handles occlusion, fast motion, and unaligned inputs, producing temporally coherent videos with fine detail preservation. Ablation studies confirm the importance of the trajectory encoding, sequence concatenation, and annealing training in achieving these results.
- Across multiple scenarios, including motion cloning, camera redirection, and mesh animation, FlexTraj consistently outperforms existing methods, validating its versatility and robustness.
Significance
This work advances controllable video generation by overcoming limitations of structural alignment and control granularity. Its multi-granularity, alignment-agnostic approach broadens the applicability of diffusion models in creative industries, virtual reality, and animation. By enabling flexible, precise motion editing without strict structural assumptions, FlexTraj addresses longstanding challenges in content creation, paving the way for more intuitive and versatile AI-driven video synthesis.
Technical Contribution
The paper introduces a novel point trajectory encoding scheme combining segmentation, temporal, and appearance attributes, integrated via a unified condition video. It develops an efficient sequence concatenation method with LoRA adaptation and causal masking, significantly improving convergence speed and control robustness. The annealing training curriculum ensures the model generalizes across diverse supervision levels, setting new standards for flexible, high-fidelity video synthesis.
Novelty
FlexTraj is the first framework supporting multi-granularity, alignment-agnostic point trajectory control within a unified diffusion-based video generation pipeline. Its innovative combination of point encoding, condition fusion, and training curriculum distinguishes it from prior works limited to either dense or sparse, aligned controls, offering a comprehensive solution for complex motion editing.
Limitations
- Despite its robustness, the model struggles with extreme unalignment or rapid scene changes, leading to artifacts or motion inconsistencies. Its performance diminishes in highly cluttered or occluded environments.
- Training requires extensive annotated datasets and synthetic data augmentation, which may limit scalability. Real-time inference at high resolutions remains challenging due to computational costs.
- Future improvements could focus on enhancing scene understanding, reducing inference latency, and extending control to more complex 3D environments.
Future Work
Future directions include integrating multi-modal conditions such as depth or semantic maps, enabling real-time high-resolution control, and extending to full 3D scene manipulation. Exploring unsupervised or weakly supervised training paradigms could further improve generalization, making the technology more accessible for broader applications.
AI Executive Summary
Controlling motion in video synthesis remains a fundamental challenge, especially when aiming for fine-grained, flexible, and unaligned editing. Existing diffusion-based methods excel in quality but lack versatile control mechanisms. FlexTraj addresses this gap by proposing a unified point trajectory representation that encodes object segmentation, temporal correspondence, and appearance cues. This compact encoding is projected into condition videos via a pretrained VAE, capturing essential motion and appearance information.
The core innovation lies in the sequence concatenation strategy, which efficiently combines condition tokens with the generative process, enhanced by LoRA fine-tuning and causal masking. This design allows the model to handle diverse control scenarios, including dense, sparse, and unaligned inputs, without structural constraints. The training adopts an annealing curriculum, gradually shifting from complete supervision to sparse and unaligned conditions, ensuring robust generalization.
Experimental results demonstrate that FlexTraj significantly outperforms state-of-the-art baselines in both quantitative metrics—such as TrajErr, TrajSIM, and FVD—and qualitative assessments. It produces high-quality, temporally coherent videos that accurately follow complex motion cues, even under occlusion and rapid movements. Its versatility makes it suitable for applications ranging from motion cloning and camera redirection to mesh animation and virtual reality content creation.
Overall, FlexTraj marks a substantial step forward in controllable diffusion-based video synthesis, offering a flexible, efficient, and robust solution that broadens the scope of AI-generated content. Despite some limitations in extreme scenarios, its innovative approach opens new avenues for research and industry deployment, promising more intuitive and powerful tools for creative professionals and researchers alike.
Deep Dive
Abstract
We present FlexTraj, a framework for image-to-video generation with flexible point trajectory control. FlexTraj introduces a unified point-based motion representation that encodes each point with a segmentation ID, a temporally consistent trajectory ID, and an optional color channel for appearance cues, enabling both dense and sparse trajectory control. Instead of injecting trajectory conditions into the video generator through token concatenation or ControlNet, FlexTraj employs an efficient sequence-concatenation scheme that achieves faster convergence, stronger controllability, and more efficient inference, while maintaining robustness under unaligned conditions. To train such a unified point trajectory-controlled video generator, FlexTraj adopts an annealing training strategy that gradually reduces reliance on complete supervision and aligned condition. Experimental results demonstrate that FlexTraj enables multi-granularity, alignment-agnostic trajectory control for video generation, supporting various applications such as motion cloning, drag-based image-to-video, motion interpolation, camera redirection, flexible action control and mesh animations.