REVIEW 4 cited by
AniDoc: Animation Creation Made Easier
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, AniDoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. Our code is available at: https://yihao-meng.github.io/AniDoc_demo.
Forward citations
Cited by 4 Pith papers
-
ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing
ToonComposer generates cartoon videos from a colored reference frame and sparse keyframe sketches, merging inbetweening and colorization in one diffusion model.
-
MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation
MagicAnime is a 400k-clip multimodal cartoon dataset with hierarchical annotations and benchmarks for image-to-video, pose-driven, face reenactment, and audio-driven animation generation.
-
AnimeColor: Reference-based Animation Colorization with Diffusion Transformers
AnimeColor colorizes animation sketch sequences from a reference image using a diffusion transformer with high-level and low-level color guidance.
-
Fuel Consumption in Platoons: A Literature Review
A literature review compiling factors that affect fuel consumption in vehicle platoons, including drag reduction, coordination, and instability.
Discussion (0). Sign in to comment.