GeRM learns a distribution transfer vector field via a multi-condition ControlNet to convert physically-based renders into photorealistic images using text prompts and a 50K expert-curated dataset.
Epic Games
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
AniGen directly generates animatable 3D assets with consistent shape, skeleton, and skinning from single images using unified S^3 fields and a two-stage flow-matching pipeline.
citing papers explorer
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GeRM: A Generative Rendering Model From Physically Realistic to Photorealistic
GeRM learns a distribution transfer vector field via a multi-condition ControlNet to convert physically-based renders into photorealistic images using text prompts and a 50K expert-curated dataset.
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AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation
AniGen directly generates animatable 3D assets with consistent shape, skeleton, and skinning from single images using unified S^3 fields and a two-stage flow-matching pipeline.