# PeCA: Palette Context Assisted Inference for Test-Time Paint-Bucket Colourisation on Animation Videos

arXiv: https://arxiv.org/abs/2608.00903 · HTML: https://mlsanity.com/paper/2608.00903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 66 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling](https://mlsanity.com/paper/2111.03930/) (2021)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Learning Correspondence from the Cycle-Consistency of Time](https://mlsanity.com/paper/1903.07593/) (2019)
- [MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning](https://mlsanity.com/paper/2309.07915/) (2023)
- [Better Aggregation in Test-Time Augmentation](https://mlsanity.com/paper/2011.11156/) (2020)
- [ToonCrafter: Generative Cartoon Interpolation](https://mlsanity.com/paper/2405.17933/) (2024)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization](https://mlsanity.com/paper/2402.17214/) (2024)
- [LVCD: Reference-based Lineart Video Colorization with Diffusion Models](https://mlsanity.com/paper/2409.12960/) (2024)
- [V-Thinker: Interactive Thinking with Images](https://mlsanity.com/paper/2511.04460/) (2025)
- [AniDoc: Animation Creation Made Easier](https://mlsanity.com/paper/2412.14173/) (2024)
- [The Animation Transformer: Visual Correspondence via Segment Matching](https://mlsanity.com/paper/2109.02614/) (2021)
- [Region-Based Representations Revisited](https://mlsanity.com/paper/2402.02352/) (2024)
- [Sakuga-42M Dataset: Scaling Up Cartoon Research](https://mlsanity.com/paper/2405.07425/) (2024)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [MangaNinja: Line Art Colorization with Precise Reference Following](https://mlsanity.com/paper/2501.08332/) (2025)
- [Learning Inclusion Matching for Animation Paint Bucket Colorization](https://mlsanity.com/paper/2403.18342/) (2024)
- [LayerAnimate: Layer-level Control for Animation](https://mlsanity.com/paper/2501.08295/) (2025)

## Abstract (arXiv)

In animation production, paint-bucket colourisation for hand-drawn animation is a labour-intensive procedure that assigns each enclosed region in line sketches a colour from reference design sheets. Recent automatic paint-bucket colourisation pipelines mirror this workflow via region correspondence, but correspondences can be brittle when regions are ambiguous fragments without proper context. In this paper, we propose Palette Context Assisted (PeCA), a new training-free, plug-and-play framework for animation video colourisation that aims to close this gap at test-time via reasoning over spatial and temporal contexts. Extensive experiments on existing benchmarks and a newly introduced long-video test case show consistent performance boosts.