✨RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale

📝 Summary:
RAISECity uses an agentic framework with multimodal tools for reality-aligned, high-quality, city-scale 3D world generation. It iteratively refines scenes, achieving superior precision and fidelity compared to existing methods.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18005
• PDF: https://arxiv.org/pdf/2511.18005
• Github: https://github.com/tsinghua-fib-lab/RAISECity

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#3DGeneration #GenerativeAI #MultimodalAI #VirtualWorlds #ComputerGraphics

✨LATTICE: Democratize High-Fidelity 3D Generation at Scale

📝 Summary:
LATTICE is a framework for high-fidelity 3D generation using VoxSet, a compact semi-structured representation. It employs a two-stage pipeline with a rectified flow transformer, achieving efficient, scalable, and high-quality 3D creation.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03052
• PDF: https://arxiv.org/pdf/2512.03052
• Project Page: https://lattice3d.github.io/
• Github: https://github.com/Zeqiang-Lai/LATTICE

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#3DGeneration #AI #DeepLearning #ComputerGraphics #GenerativeAI

✨ShadowDraw: From Any Object to Shadow-Drawing Compositional Art

📝 Summary:
ShadowDraw generates art where a 3D object's cast shadow completes a partial line drawing into a recognizable image. It optimizes object pose, lighting, and the line drawing for visual coherence and quality. This framework creates compelling shadow art and expands computational visual art design.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05110
• PDF: https://arxiv.org/pdf/2512.05110

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#ComputationalArt #ComputerGraphics #AIArt #DigitalArt #GenerativeArt

✨SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations

📝 Summary:
SCAIL is a framework that improves character animation to studio-grade quality. It uses a novel 3D pose representation and a diffusion-transformer with full-context pose injection, achieving state-of-the-art realism and reliability.

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05905
• PDF: https://arxiv.org/pdf/2512.05905

🔹 Models citing this paper:
• https://huggingface.co/zai-org/SCAIL-Preview

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#CharacterAnimation #AI #3DAnimation #DeepLearning #ComputerGraphics

✨SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting

📝 Summary:
This survey overviews efficient 3D and 4D Gaussian Splatting. It categorizes parameter and restructuring compression methods to reduce memory and computation while maintaining reconstruction quality. It also covers current limitations and future research.

🔹 Publication Date: Published on Dec 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07197
• PDF: https://arxiv.org/pdf/2512.07197
• Project Page: https://cmlab-korea.github.io/Awesome-Efficient-GS/
• Github: https://cmlab-korea.github.io/Awesome-Efficient-GS/

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#GaussianSplatting #3DVision #ComputerGraphics #DeepLearning #Efficiency

✨MeshSplatting: Differentiable Rendering with Opaque Meshes

📝 Summary:
MeshSplatting is a novel mesh-based method for real-time novel view synthesis. It uses differentiable rendering to optimize geometry and appearance, producing high-quality meshes that integrate with AR/VR pipelines. It outperforms prior methods in quality, speed, and memory.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06818
• PDF: https://arxiv.org/pdf/2512.06818
• Project Page: https://meshsplatting.github.io/
• Github: https://github.com/meshsplatting/mesh-splatting

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#DifferentiableRendering #NovelViewSynthesis #ComputerGraphics #ARVR #3DRendering

✨FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering

📝 Summary:
FrameDiffuser is an autoregressive neural rendering framework. It generates temporally consistent, photorealistic frames using G-buffer data and its own previous output. This achieves interactive speed and high quality compared to prior methods.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16670
• PDF: https://arxiv.org/pdf/2512.16670

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#NeuralRendering #DiffusionModels #ComputerGraphics #RealtimeRendering #DeepLearning

✨3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework

📝 Summary:
3D-RE-GEN reconstructs single images into modifiable 3D textured mesh scenes with comprehensive backgrounds. It uses a compositional generative framework and novel optimization for artist-ready, physically realistic layouts, achieving state-of-the-art performance.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17459
• PDF: https://arxiv.org/pdf/2512.17459
• Project Page: https://3dregen.jdihlmann.com/
• Github: https://github.com/cgtuebingen/3D-RE-GEN

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#3DReconstruction #GenerativeAI #ComputerVision #DeepLearning #ComputerGraphics

✨MatSpray: Fusing 2D Material World Knowledge on 3D Geometry

📝 Summary:
MatSpray integrates 2D PBR materials from diffusion models onto 3D Gaussian Splatting geometry. Using projection and neural refinement, it enables accurate relighting and photorealistic rendering from reconstructed scenes. This boosts asset creation efficiency.

🔹 Publication Date: Published on Dec 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18314
• PDF: https://arxiv.org/pdf/2512.18314
• Project Page: https://matspray.jdihlmann.com/
• Github: https://github.com/cgtuebingen/MatSpray

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#MatSpray #GaussianSplatting #DiffusionModels #3DRendering #ComputerGraphics

✨Over++: Generative Video Compositing for Layer Interaction Effects

📝 Summary:
Over++ introduces augmented compositing, a framework that generates realistic, text-prompted environmental effects for videos. It synthesizes effects like shadows onto video layers while preserving the original scene, outperforming prior methods without dense annotations.

🔹 Publication Date: Published on Dec 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19661
• PDF: https://arxiv.org/pdf/2512.19661
• Project Page: https://overplusplus.github.io/

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#GenerativeAI #VideoCompositing #VFX #ComputerGraphics #AIResearch

✨Yume-1.5: A Text-Controlled Interactive World Generation Model

📝 Summary:
Yume-1.5 is a novel framework that generates realistic, interactive, and continuous worlds from a single image or text prompt. It overcomes prior limitations in real-time performance and text control by using unified context compression, streaming acceleration, and text-controlled world events.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22096
• PDF: https://arxiv.org/pdf/2512.22096
• Project Page: https://stdstu12.github.io/YUME-Project/
• Github: https://github.com/stdstu12/YUME

🔹 Models citing this paper:
• https://huggingface.co/stdstu123/Yume-5B-720P

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#AI #GenerativeAI #WorldGeneration #ComputerGraphics #DeepLearning

✨UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/

🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape

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#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning

✨SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time

📝 Summary:
SpaceTimePilot is a video diffusion model for dynamic scene rendering, offering independent control over spatial viewpoint and temporal motion. It achieves precise space-time disentanglement via a time-embedding, temporal-warping training, and a synthetic dataset.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25075
• PDF: https://arxiv.org/pdf/2512.25075
• Project Page: https://zheninghuang.github.io/Space-Time-Pilot/

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#VideoDiffusion #GenerativeAI #DynamicScenes #ComputerGraphics #DeepLearning