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GaussianStyle: Gaussian Head Avatar via StyleGAN
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GaussianStyle: Gaussian Head Avatar via StyleGAN
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Existing methods like Neural Radiation Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made significant strides in facial attribute control such as facial animation and components editing, yet they struggle with fine-grained representation and scalability in dynamic head modeling. To address these limitations, we propose GaussianStyle, a novel framework that integrates the volumetric strengths of 3DGS with the powerful implicit representation of StyleGAN. The GaussianStyle preserves structural information, such as expressions and poses, using Gaussian points, while projecting the implicit volumetric representation into StyleGAN to capture high-frequency details and mitigate the over-smoothing commonly observed in neural texture rendering. Experimental outcomes indicate that our method achieves state-of-the-art performance in reenactment, novel view synthesis, and animation.
Forward citations
Cited by 6 Pith papers
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench curates 360 ground-truth clips and an evaluation suite to diagnose memory consistency failures in video models when objects change state while out of view.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench is a new diagnostic benchmark with 360 synthetic and real clips plus VQA evaluation that tests memory consistency in video models under the disappear-and-reappear paradigm in dynamically changing environments.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench is a new diagnostic benchmark with automated and VQA metrics that evaluates memory consistency in video models under disappear-and-reappear in dynamic environments.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
Current video world models do not reliably recover an object's updated state after it disappears and reappears under simultaneous camera and scene dynamics.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
None of ten tested video-generation models reliably remembers objects after occlusion in dynamic scenes; static-camera videos inflate consistency scores.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench curates 360 clips and an evaluation suite to test video models on recovering updated object states after disappear-and-reappear in changing environments.
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