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Image Conductor: Precision Control for Interactive Video Synthesis

Yaowei Li, Xintao Wang, Zhaoyang Zhang, Zhouxia Wang, Ziyang Yuan, Liangbin Xie, Yuexian Zou, Ying Shan

arXiv:2406.15339v1cs.CVcs.AIcs.MM

TL;DR

Precise control of camera transitions and object movements remains challenging in video generation, despite its importance for interactive video creation. Image Conductor combines trajectory-oriented training data, distinct camera and object LoRAs, and camera-free guidance. Experiments report precise, fine-grained control, while output quality remains constrained by the base model and conflicting prompts may compromise quality.

  • Problem

    Video generation still lacks fine-grained, accurate control over camera transitions and object movements, limiting interactive creation from images.

    Source: §1 · p. 2

  • Method

    Image Conductor curates trajectory-annotated videos, trains distinct camera and object LoRAs, and uses camera-free guidance during inference.

    Source: §1 · p. 2, §2.2 · p. 4

  • Results

    Extensive experiments demonstrate precise, fine-grained motion control for generating videos from images aligned with user desires.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Image Conductor is presented as a step toward practical video-centric creative expression through controlled camera and object motion.

    Source: §4 · p. 9

    Output quality may be compromised when text and image prompts convey different meanings.

    Source: D LIMITATIONS · p. 15

Abstract

from arXiv · show

Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset generation remains challenging. To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image. An well-cultivated training strategy is proposed to separate distinct camera and object motion by camera LoRA weights and object LoRA weights. To further address cinematographic variations from ill-posed trajectories, we introduce a camera-free guidance technique during inference, enhancing object movements while eliminating camera transitions. Additionally, we develop a trajectory-oriented video motion data curation pipeline for training. Quantitative and qualitative experiments demonstrate our method's precision and fine-grained control in generating motion-controllable videos from images, advancing the practical application of interactive video synthesis. Project webpage available at https://liyaowei-stu.github.io/project/ImageConductor/

1 INTRODUCTION

Image Conductor addresses the difficulty of precisely controlling camera transitions and object movements in video generation, a task that current methods and costly production workflows do not adequately support. It combines trajectory-oriented data, distinct motion LoRAs, and camera-free guidance; experiments report precise, fine-grained motion control.

1 INTRODUCTION

  • Current video-generation methods lack an efficient control interface and fine-grained, accurate control over both camera transitions and object movements.Existing workflows also rely heavily on labor-intensive and costly real-world capture and 3D scan modeling.Source: §1 · p. 2
  • The authors curate trajectory-oriented video data with precise annotations, addressing the lack of high-quality open-source trajectory-based tracking data.The framework pairs image conditioning for appearance with motion-controllable ControlNet and its LoRA weights for motion information.Source: §1 · p. 2, Figure 2 · p. 3
  • Image Conductor uses distinct LoRA weights to separate and control camera transitions and object movements.Its collaborative optimization applies an orthogonal loss to encourage independence between the LoRA weights.Source: §1 · pp. 2–3
  • Camera-free guidance heuristically removes camera transitions caused by multiple trajectories that are difficult to separate with LoRA weights.During diffusion sampling, it iteratively extrapolates and fuses different latents to enhance object movement.Source: §1 · pp. 2–3
  • Extensive experiments demonstrate precise, fine-grained motion control for generating image-based videos aligned with user desires.The introduction reports this outcome without providing quantitative benchmark values.Source: §1 · p. 3

2 APPROACH

Image Conductor uses user-drawn trajectories to guide camera and object motion from an image. Its approach combines curated trajectory video data, a motion-aware image-to-video architecture, staged LoRA training, and camera-free guidance.

2.1 OVERVIEW

  • User-friendly trajectories specify the intensity and direction of camera transitions and object movements, while separate camera and object LoRAs support their control.The camera LoRA is trained first; the object LoRA is then trained with the camera weights loaded, with orthogonal constraints to maintain independence.Source: §2.1 · p. 3

2.2 TRAJECTORY-ORIENTED VIDEO MOTION DATA CONSTRUCTION.

  • The data pipeline filters for consistent scenes and motion, tracks dense points with CoTracker, then samples and smooths sparse trajectories for training.It produces 130k mixed-motion videos and 62k camera-only videos; the pipeline also detects cuts, selects the longest consistent scenes, and removes the lowest 25% by motion score.Source: §2.2 · p. 4

2.3 MOTION-AWARE IMAGE-TO-VIDEO ARCHITECTURE

  • Animatediff with image SparseCtrl provides the image-to-video backbone, while motion-controllable ControlNet encodes trajectory input and uses LoRAs for camera and object motion.Removing skip connections between the main and conditional branches' input latents speeds convergence during training.Source: §2.3 · pp. 4–5

2.4 CONTROLLABLE MOTION SEPARATION

  • Camera LoRA training on camera-only data precedes object LoRA training on mixed-motion data, with gradients stopped through the loaded camera LoRA.An orthogonal loss constrains the camera and object LoRA weights; the staged procedure is also shown in Figure 3.Source: §2.4 · pp. 5–6, Figure 3 · p. 5

2.5 CAMERA-FREE GUIDANCE

  • Camera-free guidance extrapolates between object-LoRA and pretrained-ControlNet outputs to enhance motion intensity while eliminating camera transitions.It addresses camera transitions that multiple trajectories can introduce when users control multiple objects.Source: §2.5 · p. 6

3 EXPERIMENTS

Image Conductor is evaluated against motion-controllable video methods, then tested for personalization and for the effects of its separate LoRA weights and camera-free guidance.

3.1 COMPARISONS WITH STATE-OF-THE-ART METHODS

  • Qualitatively, Image Conductor controls camera transitions and object movements, while compared methods miss some camera paths or confuse object motion with camera motion.MotionCtrl-SVD also cannot define natural content changes through text prompts and may produce distortion artifacts.Source: §3.1 · pp. 6–7, Figure 4 · p. 7
  • Image Conductor achieves state-of-the-art quantitative performance across trajectory-alignment and video-quality metrics, while human evaluations favor its realism, visual appeal, and motion adherence.The reported evaluation uses FID, FVD, CamMC, and ObjMC, alongside human judgments of overall performance, sample quality, and motion similarity.Source: §3.1 · pp. 7–8, Table 1 · p. 8

3.2 PERSONALIZED AND CONTROLLABLE VIDEO SYNTHESIS

  • Image Conductor integrates with personalized models while retaining controllability through prompts and user-specified trajectories.The pre-trained base model is not fine-tuned; the tested checkpoints include TuSun 2, HelloObject 3, and CardosAnime 4.Source: §3.2 · p. 8, Figure 5 · p. 8

3.3 ABLATION STUDIES

  • With the same upward trajectory, the camera LoRA produces an upward pan, whereas the object LoRA produces upward waves.This comparison tests whether distinct LoRA weights separate camera transitions from object movements.Source: §3.3 · pp. 8–9, Figure 6 · p. 9
  • Camera-free guidance makes object movements more apparent as guidance increases, though artifacts remain at a setting of 1.1.At λtrajs = 1, the video instead exhibits an unexpected pan left.Source: §3.3 · p. 9, Figure 7 · p. 9

4 CONCLUSION

The conclusion presents Image Conductor as a method for fine-grained control of camera transitions and object movements, supported by a training strategy and camera-free guidance.

4 CONCLUSION

  • Image Conductor targets precise, fine-grained control of camera transitions and object movements in interactive video synthesis.The conclusion identifies this as the method's central contribution.Source: §4 · p. 9
  • Distinct LoRA weights are used to decouple camera transitions from object movements.Source: §4 · p. 9
  • The authors report that extensive experiments demonstrate the method's effectiveness and its relevance to practical video-centric creative expression.Source: §4 · p. 9

A RELATED WORKS

Prior video-synthesis methods face controllability challenges, and motion-control studies have not precisely and finely manipulated visual elements, especially object movements.

Video Synthesis

  • Video-generation methods still face randomness and difficulty producing high-quality videos with controllability, hindering practical application of AIGC-based video generation.Source: A RELATED WORKS · p. 13

Motion Control in Videos

  • Prior motion-control methods use trajectories, camera parameters, or bounding boxes, but lack precise, fine-grained manipulation, especially of object movements.The paper says its training strategy uses existing data for flexible and precise motion separation and control.Source: A RELATED WORKS · p. 13

B.1 CONDITIONAL VIDEO DIFFUSION MODEL

The section describes diffusion as progressive noise addition followed by learned denoising, and explains conditional video generation and LoRA’s parameter-efficient weight updates.

B.1 CONDITIONAL VIDEO DIFFUSION MODEL

  • Diffusion models add Gaussian noise to a signal through a forward Markov chain and use a learned reverse process to iteratively denoise it.The conditional model samples a video sequence from p(x0|c) using a denoising UNet optimized via denoising score matching.Source: §B.1 · p. 13
  • In the variance-preserving setting, the schedule parameter α_t lies strictly between 0 and 1; the cited forward-process distributions q(x_t | x_{t−1}) and q(x_t | x_0) are given in Eq. 6.Source: §B.1 · p. 13

B.2 LOW-RANK ADAPTATION

  • LoRA fine-tunes a model by adding learnable low-rank matrices to layer weights while keeping the original weights fixed.The rank r is smaller than min(m,n), reducing fine-tuning cost.Source: §B.2 · p. 14

C.1 IMPLEMENTATION DETAILS.

Implementation uses a frozen UNet with a trained motion ControlNet, then learns camera and object LoRAs using a staged data strategy.

C.1 IMPLEMENTATION DETAILS.

  • The method uses AnimateDiff v3 with RGB SparseCtrl and trains only the motion ControlNet, keeping the UNet backbone frozen.Training uses sampled 16-frame videos at 384 × 256 resolution.Source: §C.1 · p. 14
  • Camera and object LoRAs are optimized on eight Tesla V100 GPUs for a week, with batch size 64 and learning rate 1 × 10^-4.The ControlNet is first trained on mixed data; camera LoRA uses camera-only data, while object LoRA uses mixed data.Source: §C.1 · p. 14
  • Training progresses from dense trajectories to sparse-trajectory fine-tuning, and inference uses 25 DDIM steps with classifier-free guidance scale 8.5.The guidance scale is 8.5 unless otherwise noted.Source: §C.1 · p. 14

C.2 EVALUATION METRICS.

Evaluation measures generated-video content and motion control, using distributional video metrics and trajectory distances.

C.2 EVALUATION METRICS.

  • FID and FVD measure visual quality and temporal coherence, using 5,000 randomly selected WebVid videos as reference.The evaluation follows MotionCtrl’s two metric categories.Source: §C.2 · p. 14
  • CamMC and ObjMC evaluate motion control as the Euclidean distance between predicted and ground-truth trajectories.CamMC is computed from pixel-level movement trajectories rather than camera poses estimated with ParticleSfM.Source: §C.2 · p. 14
  • Unlike MotionCtrl, the method computes CamMC from directly extracted pixel-level movement trajectories, as it does for ObjMC.MotionCtrl estimates predicted-video camera poses with ParticleSfM.Source: §C.2 · p. 14

D LIMITATIONS

The method’s generated content quality remains constrained by its base model, and conflicting text and image prompts may compromise output quality.

D LIMITATIONS

  • Generated content quality remains constrained by the base model despite faithful motion following of user-input trajectories.The base model can produce color and detail inconsistencies between the first frame and later frames.Source: D LIMITATIONS · p. 15
  • One proposed remedy for frame inconsistency is concatenating noisy image latents with input noise, alongside image-conditioning injection.The paper notes similar approaches in SVD and DynamiCrafter.Source: D LIMITATIONS · p. 15
  • When text and image prompts convey different meanings, output quality may be compromised.Source: D LIMITATIONS · p. 15
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