VIRES: Video Instance Repainting via Sketch and Text Guided Generation¶

Conference: CVPR 2025
Code: TBD
Area: LLM Pre-training
Keywords: TBD

TL;DR¶

Based on abstract: We introduce VIRES, a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generat

Background & Motivation¶

Background: The problem studied in this paper falls under the direction of NLP understanding. We introduce VIRES, a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generative priors of text-to-video models to maintain temporal consistency and produce visually pleasing results.
Limitations of Prior Work: Existing methods have limitations, with room for improvement in efficiency, accuracy, or generalization.
Key Challenge: There is a need to find a better balance between performance and efficiency/generalization.
Goal: To address the aforementioned issues, the authors propose a new method.
Key Insight: Starting from a new technical perspective or observation.
Core Idea: We propose the Sequential ControlNet with the standardized self-scaling, which effectively extracts structure layouts and adaptively captures high-contrast sketch details. We further augment the diffu

Method¶

Overall Architecture¶

An overview of the proposed method is as follows (based on abstract information):

We propose the Sequential ControlNet with the standardized self-scaling, which effectively extracts structure layouts and adaptively captures high-contrast sketch details. We further augment the diffusion transformer backbone with the sketch attention to interpret and inject fine-grained sketch semantics. A sketch-aware encoder ensures that repainted results are aligned with the provided sketch sequence.

Key Designs¶

Sequential ControlNet + Standardized Self-scaling:
- Function: Effectively extracts structural layouts from the sketch sequence
- Mechanism: Introduces a standardized self-scaling mechanism in ControlNet to adaptively capture high-contrast sketch details, extracting structural layouts frame-by-frame while maintaining temporal consistency
- Design Motivation: Standard ControlNet is not sensitive enough to high-contrast edges of sketches; self-scaling can dynamically adjust feature responses
Sketch Attention:
- Function: Injects fine-grained sketch semantics into the diffusion Transformer backbone
- Mechanism: Adds a dedicated sketch attention layer to the DiT backbone to interpret semantic information such as contours and shapes of the sketch, injecting them into the video generation process
- Design Motivation: Standard text conditioning cannot precisely control shape details, necessitating an additional channel for sketch condition injection
Sketch-aware Encoder:
- Function: Ensures precise alignment of the repainted results with the provided sketch sequence
- Mechanism: Encodes spatiotemporal features of the sketch sequence, providing frame-consistent shape constraints
- Design Motivation: Guarantees that the shape and motion of the repainted targets are consistent with the user-specified sketch sequence

Loss & Training¶

Fine-tuning is performed based on the generative priors of a text-to-video model, utilizing detailed annotations from the VireSet dataset for supervised training.

Key Experimental Results¶

Main Results¶

Comprehensive evaluation on the VireSet dataset demonstrates that VIRES outperforms state-of-the-art (SOTA) approaches across four dimensions: visual quality, temporal consistency, conditional alignment, and human evaluation.

Evaluation Metric	VIRES	SOTA Baseline	Description
Visual Quality	Best	Second Best	Metrics such as FID/LPIPS
Temporal Consistency	Best	Second Best	Inter-frame consistency
Conditional Alignment	Best	Second Best	Sketch-result alignment
Human Rating	Best	Second Best	Subjective quality

Ablation Study¶

Configuration	Effect	Description
W/o Sketch Attention	Shape distortion	Unable to inject fine-grained sketch semantics
W/o Standardized Self-scaling	Detail loss	Insufficient capture of high-contrast sketch features
W/o Sketch-aware Encoder	Alignment degradation	Shift between repainted results and sketch sequences

Key Findings¶

The three modules (Sequential ControlNet, Sketch Attention, and Sketch-aware Encoder) provide complementary contributions.
The method supports four operational modes: instance repainting, replacement, generation, and removal, demonstrating high versatility.

Highlights & Insights¶

Clearly defined problem with highly targeted approaches.
The core design ideas could likely be transferred to related scenarios.
Sequential ControlNet combined with the standardized self-scaling mechanism effectively extracts structural layouts and adaptively captures high-contrast sketch details.
The Sketch Attention mechanism's enhancement of the diffusion Transformer backbone achieves the injection of fine-grained sketch semantics.
Proposes the VireSet dataset, which contains detailed annotations for the training and evaluation of video instance editing.
The method supports four operational modes: instance repainting, replacement, generation, and removal, offering strong versatility.

Limitations & Future Work¶

Acquiring sketch sequences in practical applications may require users to draw manually, leading to higher interaction costs.
Sketch-to-video alignment may still be difficult for complex motion patterns (e.g., rapid rotation, high deformation).
Future research could explore automatically generating sketch sequences from text descriptions to lower the barrier to entry.
The scale and diversity of the VireSet dataset can be further expanded.
The ability of the method to maintain temporal consistency in longer videos (>100 frames) remains to be validated.

This work makes improvements on existing methods in the field of video instance editing.
Compared to text-only guided video editing, sketch conditioning provides more precise shape control.
The VireSet dataset provides a new benchmark for video instance editing research.

Rating¶

Novelity: ⭐⭐⭐⭐ Preliminary rating based on the abstract, showing notable innovation.
Experimental Thoroughness: ⭐⭐⭐⭐ Needs full-text verification.
Writing Quality: ⭐⭐⭐⭐ Preliminary rating based on the abstract.
Value: ⭐⭐⭐⭐ Contributes to the field.