EDGAR: Efficient Diffusion-Driven Gaussian Avatar Refinement via Synthetic Failure
The paper presents EDGAR, a conditional diffusion framework that refines artifacts in Gaussian head avatars while preserving identity.
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The paper presents EDGAR, a conditional diffusion framework that refines artifacts in Gaussian head avatars while preserving identity. It uses EDGAR-SET, a hybrid dataset of real-captured and synthetic-rendered degraded samples, and a fusion block injecting identity features from the Sapiens model into a one-step diffusion backbone. A Mixture-of-Experts architecture handles varied degradation types. Refined multi-view renderings serve as pseudo-targets to optimize a single avatar. Reported experiments cover three avatar reconstruction methods and an SDEdit-style baseline.
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EDGAR refines degraded Gaussian avatar renderings with a conditional diffusion framework using a one-step diffusion backbone.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseEDGAR-SET pairs degraded renderings with ground-truth and reference images, combining real-captured and synthetic-rendered samples.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseRefined multi-view renderings are used as pseudo-targets to optimize one Gaussian avatar, avoiding per-frame EDGAR application.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe paper reports perceptual and reconstruction gains over raw outputs and an SDEdit-style baseline across three reconstruction methods.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Artifacts such as blurring and temporal instability limit photorealistic monocular head avatars. A method that corrects these artifacts efficiently, and bakes the fix into a single avatar, could make avatar pipelines faster at render time.
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