✨Scaling Behavior of Discrete Diffusion Language Models

📝 Summary:
Research on discrete diffusion language models DLMs shows their scaling behavior depends on noise type. Uniform diffusion is more parameter and data efficient than masked diffusion, making it promising for data-bound settings. A 10B parameter model confirmed this.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10858
• PDF: https://arxiv.org/pdf/2512.10858
• Github: https://github.com/dvruette/gidd-easydel

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

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#DiffusionModels #LanguageModels #NLP #AIResearch #DeepLearning

✨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

✨RadarGen: Automotive Radar Point Cloud Generation from Cameras

📝 Summary:
RadarGen synthesizes realistic automotive radar point clouds from camera images using diffusion models. It incorporates depth, semantic, and motion cues for physical plausibility, enabling scalable multimodal simulation and improving perception models.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17897
• PDF: https://arxiv.org/pdf/2512.17897

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

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#AutomotiveRadar #PointClouds #DiffusionModels #ComputerVision #AutonomousDriving

✨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

✨SkyReels-V2: Infinite-length Film Generative Model

📝 Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2

🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P

✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
• https://huggingface.co/spaces/14eee109giet/SkyReels-V2

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

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#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch

✨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion

📝 Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17504
• PDF: https://arxiv.org/pdf/2512.17504
• Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
• Github: https://github.com/myyzzzoooo/InsertAnywhere

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

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#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI

✨LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation

📝 Summary:
LiveTalk enables real-time multimodal interactive video generation from text, image, and audio by improving on-policy diffusion distillation. It reduces inference latency by 20x while maintaining quality, allowing seamless human-AI interaction.

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23576
• PDF: https://arxiv.org/pdf/2512.23576
• Github: https://github.com/GAIR-NLP/LiveTalk

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

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#VideoGeneration #AI #DiffusionModels #RealTimeAI #MultimodalAI

✨Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation

📝 Summary:
Transparent objects are hard for perception. This work observes video diffusion models can synthesize transparent phenomena, so they repurpose one. Their DKT model, trained on a new dataset, achieves zero-shot SOTA for depth and normal estimation of transparent objects, proving diffusion knows tr...

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23705
• PDF: https://arxiv.org/pdf/2512.23705
• Project Page: https://daniellli.github.io/projects/DKT/
• Github: https://github.com/Daniellli/DKT

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

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#ComputerVision #DiffusionModels #DepthEstimation #TransparentObjects #AIResearch

✨SpotEdit: Selective Region Editing in Diffusion Transformers

📝 Summary:
SpotEdit is a training-free framework for selective image editing in diffusion transformers. It avoids reprocessing stable regions by reusing their features, combining them with edited areas. This reduces computation and preserves unchanged regions, enhancing efficiency and precision.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22323
• PDF: https://arxiv.org/pdf/2512.22323
• Project Page: https://biangbiang0321.github.io/SpotEdit.github.io
• Github: https://biangbiang0321.github.io/SpotEdit.github.io

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

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#ImageEditing #DiffusionModels #ComputerVision #AIResearch #DeepLearning

✨Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone

📝 Summary:
Dream-VL and Dream-VLA are diffusion-based vision-language and vision-language-action models. They achieve state-of-the-art performance in visual planning and robotic control, surpassing autoregressive baselines via their diffusion backbone's superior action generation.

🔹 Publication Date: Published on Dec 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22615
• PDF: https://arxiv.org/pdf/2512.22615
• Project Page: https://hkunlp.github.io/blog/2025/dream-vlx/
• Github: https://github.com/DreamLM/Dream-VLX

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

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#VisionLanguageModels #DiffusionModels #Robotics #AI #ComputerVision

✨GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models

📝 Summary:
GRAN-TED improves text encoders for diffusion models by addressing evaluation and adaptation challenges. It introduces TED-6K, an efficient text-only benchmark that predicts generation quality 750x faster. Using this, GRAN-TED develops a superior encoder via a two-stage training method, enhancing...

🔹 Publication Date: Published on Dec 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15560
• PDF: https://arxiv.org/pdf/2512.15560

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

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#DiffusionModels #TextEmbeddings #AIResearch #MachineLearning #NLP

✨DiRL: An Efficient Post-Training Framework for Diffusion Language Models

📝 Summary:
DiRL is an efficient post-training framework for Diffusion Language Models, integrating online updates and introducing DiPO for unbiased policy optimization. It achieves state-of-the-art math performance for dLLMs, surpassing comparable models.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22234
• PDF: https://arxiv.org/pdf/2512.22234
• Github: https://github.com/OpenMOSS/DiRL

🔹 Models citing this paper:
• https://huggingface.co/OpenMOSS-Team/DiRL-8B-Instruct

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

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#DiffusionModels #LLM #ModelOptimization #MachineLearning #AI

✨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

✨GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction

📝 Summary:
GaMO improves sparse-view 3D reconstruction by using geometry-aware multi-view outpainting. It expands existing views to enhance scene coverage and consistency. This achieves state-of-the-art quality 25x faster than prior methods, with reduced computational cost.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25073
• PDF: https://arxiv.org/pdf/2512.25073
• Project Page: https://yichuanh.github.io/GaMO/
• Github: https://yichuanh.github.io/GaMO/

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

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#3DReconstruction #ComputerVision #DiffusionModels #GaMO #AI

✨Guiding a Diffusion Transformer with the Internal Dynamics of Itself

📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance

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

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#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision

✨Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation

📝 Summary:
DiffusionGS is a novel single-stage 3D diffusion model that directly generates 3D Gaussian point clouds from a single image. It ensures strong view consistency from any prompt view. This method achieves superior quality and is over 5x faster than state-of-the-art techniques.

🔹 Publication Date: Published on Nov 21, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.14384
• PDF: https://arxiv.org/pdf/2411.14384
• Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
• Github: https://github.com/caiyuanhao1998/Open-DiffusionGS

🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/DiffusionGS

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/DiffusionGS

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

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#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch

✨OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

📝 Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...

🔹 Publication Date: Published on Jun 29, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.23361
• PDF: https://arxiv.org/pdf/2506.23361
• Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
• Github: https://github.com/caiyuanhao1998/Open-OmniVCus

🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/OmniVCus

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train

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

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#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision