Recent advances in Gaussian splatting have enabled efficient and photorealistic head avatar reconstruction, yet monocular video-based or image-based methods still suffer from under-constrained geometr
EDGAR is a conditional diffusion framework with a fusion block and Mixture-of-Experts that refines degraded Gaussian head avatars by injecting self-supervised identity features, using refined multi-view renderings as pseudo-targets to optimize a single avatar.
Recent advances in Gaussian splatting have enabled efficient and photorealistic head avatar reconstruction, yet monocular video-based or image-based methods still suffer from under-constrained geometry and texture, leading to artifacts such as blurring, stretching, and temporal instability. We present EDGAR (Efficient Diffusion-Driven Gaussian Avatar Refinement), a conditional diffusion framework designed to enhance the quality of Gaussian avatars by correcting artifacts while preserving identity fidelity. To train EDGAR, we construct EDGAR-SET, a hybrid triplet dataset composed of real-captured and synthetic-rendered samples, which simulates realistic Gaussian avatar failure cases by…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
EDGAR: Efficient Diffusion-Driven Gaussian Avatar Refinement via Synthetic Failure was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 30 checks on 3 different days. Its best position was #17 of 30, reached on Nov 7, 09:28 UTC. It is #18 right now, holding steady at #18 over the last 6 checks.
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Yes. As of Nov 8, 15:28 UTC it is #18, holding steady at #18 over the last 6 checks.
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It was first seen on Nov 6, 2026 and has been on the list on 3 days (30 checks) since.