✨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

✨MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing

📝 Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...

🔹 Publication Date: Published on Jan 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00204
• PDF: https://arxiv.org/pdf/2601.00204
• Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
• Github: https://github.com/XiaokunSun/MorphAny3D

==================================

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#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch

✨Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training

📝 Summary:
Muses is a training-free method for generating fantastic 3D creatures. It leverages 3D skeletal structures and graph-constrained reasoning to coherently design, compose, and assemble diverse elements. This approach achieves state-of-the-art visual fidelity and alignment with text descriptions.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03256
• PDF: https://arxiv.org/pdf/2601.03256
• Github: https://github.com/luhexiao/Muses

==================================

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#3DGeneration #GenerativeAI #ComputerGraphics #AIArt #TrainingFreeAI

✨ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion

📝 Summary:
ActionMesh extends 3D diffusion models with a temporal axis to generate high-quality, rig-free animated 3D meshes. This 'temporal 3D diffusion' framework quickly creates topology-consistent animations from various inputs like video or text, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16148
• PDF: https://remysabathier.github.io/actionmesh/actionmesh_2026.pdf
• Project Page: https://remysabathier.github.io/actionmesh/
• Github: https://github.com/facebookresearch/actionmesh

🔹 Models citing this paper:
• https://huggingface.co/facebook/ActionMesh

✨ Spaces citing this paper:
• https://huggingface.co/spaces/facebook/ActionMesh

==================================

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#3DAnimation #DiffusionModels #ComputerGraphics #DeepLearning #3DModeling

✨Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing

📝 Summary:
Interp3D is a training-free framework for textured 3D morphing. It solves existing issues of structural misalignment and texture blurring by ensuring geometric consistency and texture alignment using generative priors and progressive alignment. The method outperforms prior approaches.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14103
• PDF: https://arxiv.org/pdf/2601.14103
• Project Page: https://interp3d.github.io/
• Github: https://github.com/xiaolul2/Interp3D

==================================

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#3DMorphing #GenerativeAI #ComputerGraphics #DeepLearning #AIResearch

✨PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction

📝 Summary:
PLANING is an efficient streaming 3D reconstruction framework. It combines explicit geometric primitives and neural Gaussians with decoupled optimization, achieving both high-quality rendering and accurate geometry. It outperforms prior methods in quality and speed.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22046
• PDF: https://arxiv.org/pdf/2601.22046

==================================

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#3DReconstruction #ComputerVision #NeuralNetworks #StreamingTech #ComputerGraphics

✨Implicit neural representation of textures

📝 Summary:
This work designs new texture implicit neural representations that operate continuously over UV coordinate space. Experiments show they achieve good image quality while balancing memory and rendering time, useful for real-time rendering and downstream tasks.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02354
• PDF: https://arxiv.org/pdf/2602.02354
• Project Page: https://peterhuistyping.github.io/INR-Tex/
• Github: https://github.com/PeterHUistyping/INR-Tex

==================================

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#ImplicitNeuralRepresentations #ComputerGraphics #DeepLearning #TextureModeling #RealTimeRendering

✨FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow

📝 Summary:
FlowScene is a generative model that uses multimodal graph conditioning and rectified flow to create realistic, style-consistent indoor scenes. It offers fine-grained control over object shapes, textures, and relations, surpassing prior methods.

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19598
• PDF: https://arxiv.org/pdf/2603.19598

==================================

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#GenerativeAI #3DSceneGeneration #MultimodalAI #DeepLearning #ComputerGraphics

✨F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting

📝 Summary:
F4Splat introduces predictive densification for 3D Gaussian splatting, adaptively allocating Gaussians based on spatial complexity and view overlap. This reduces redundant Gaussians, leading to compact, high-quality 3D representations with significantly fewer Gaussians than prior feed-forward met...

🔹 Publication Date: Published on Mar 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21304
• PDF: https://arxiv.org/pdf/2603.21304
• Project Page: https://mlvlab.github.io/F4Splat/
• Github: https://github.com/mlvlab/F4Splat

==================================

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#3DGaussianSplatting #ComputerGraphics #3DReconstruction #MachineLearning #NeuralRendering

✨WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation

📝 Summary:
WorldFlow3D generates unbounded 3D worlds by modeling 3D data distributions as a flow matching problem. This latent-free approach achieves rapid convergence and high-quality generation with controllable geometric and texture properties. It outperforms existing methods on both real and synthetic s...

🔹 Publication Date: Published on Mar 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29089
• PDF: https://arxiv.org/pdf/2603.29089
• Project Page: https://princeton-computational-imaging.github.io/WorldFlow3D/

==================================

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#3DGeneration #GenerativeAI #FlowMatching #ComputerGraphics #AIResearch

✨Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

📝 Summary:
This tutorial introduces Hunyuan3D 2.1, a system for generating high-fidelity, textured 3D assets to make AI content creation more accessible. It details the full workflow from data preparation to deployment, using Hunyuan3D-DiT for shape and Hunyuan3D-Paint for texture synthesis.

🔹 Publication Date: Published on Jun 18, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.15442
• PDF: https://arxiv.org/pdf/2506.15442
• Github: https://github.com/huggingface/huggingface.js

🔹 Models citing this paper:
• https://huggingface.co/tencent/Hunyuan3D-2.1
• https://huggingface.co/tencent/Hunyuan3D-Omni
• https://huggingface.co/tencent/HY3D-Bench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/tencent/HY3D-Bench

✨ Spaces citing this paper:
• https://huggingface.co/spaces/duranponce/ai-default
• https://huggingface.co/spaces/AliothTalks/Hunyuan3D-2.1
• https://huggingface.co/spaces/joaojack/Hunyuan3D-2.1

==================================

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#3DGeneration #AI #ComputerGraphics #ImageTo3D #PBRMaterials

✨Repurposing 3D Generative Model for Autoregressive Layout Generation

📝 Summary:
LaviGen is a 3D layout generation framework that repurposes 3D generative models. It uses an adapted 3D diffusion model for autoregressive generation, explicitly modeling geometric relations and physical constraints. This achieves superior, more plausible 3D layouts 65% faster than previous methods.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16299
• PDF: https://arxiv.org/pdf/2604.16299
• Project Page: https://fenghora.github.io/LaviGen-Page/
• Github: https://github.com/fenghora/LaviGen

==================================

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#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning