✨DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies

📝 Summary:
DiffProxy generates multi-view consistent human proxies using diffusion models to improve human mesh recovery. This bridges synthetic training and real-world generalization, achieving state-of-the-art performance on real benchmarks.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02267
• PDF: https://arxiv.org/pdf/2601.02267
• Project Page: https://wrk226.github.io/DiffProxy.html
• Github: https://github.com/wrk226/DiffProxy

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#HumanMeshRecovery #DiffusionModels #ComputerVision #DeepLearning #AI

✨Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping

📝 Summary:
Prithvi-CAFE improves flood mapping by integrating a pretrained Geo-Foundation Model encoder with a parallel CNN branch featuring attention modules. This hybrid approach effectively captures both global context and critical local details, achieving state-of-the-art results on Sen1Flood11 and Floo...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02315
• PDF: https://arxiv.org/pdf/2601.02315
• Github: https://github.com/Sk-2103/Prithvi-CAFE

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#FloodMapping #DeepLearning #GeoAI #RemoteSensing #ComputerVision

✨ExposeAnyone: Personalized Audio-to-Expression Diffusion Models Are Robust Zero-Shot Face Forgery Detectors

📝 Summary:
ExposeAnyone is a self-supervised diffusion model for deepfake detection that personalizes to subjects and uses reconstruction errors to measure identity distance. It significantly outperforms prior methods on unseen manipulations, including Sora2 videos, and is robust to real-world corruptions.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02359
• PDF: https://arxiv.org/pdf/2601.02359
• Github: https://mapooon.github.io/ExposeAnyonePage/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mapooon/S2CFP

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#DeepfakeDetection #DiffusionModels #ComputerVision #AITechnology #ForgeryDetection

✨RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization

📝 Summary:
RGS-SLAM is a robust Gaussian-splatting SLAM framework that uses a one-shot, correspondence-to-Gaussian initialization with DINOv3 descriptors. This method improves stability, accelerates convergence, and yields higher rendering fidelity and accuracy compared to existing systems.

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00705
• PDF: https://arxiv.org/pdf/2601.00705

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#SLAM #GaussianSplatting #ComputerVision #Robotics #DeepLearning

✨Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction

📝 Summary:
Gen3R combines reconstruction and video diffusion models to generate 3D scenes. It produces RGB videos and 3D geometry by aligning geometric and appearance latents. This achieves state-of-the-art results and improves reconstruction robustness.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04090
• PDF: https://arxiv.org/pdf/2601.04090
• Project Page: https://xdimlab.github.io/Gen3R/
• Github: https://xdimlab.github.io/Gen3R/

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#3DGeneration #DiffusionModels #ComputerVision #3DReconstruction #DeepLearning

✨RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

📝 Summary:
RL-AWB is a novel framework combining statistical methods with deep reinforcement learning for improved nighttime auto white balance. It is the first RL approach for color constancy, mimicking expert tuning. This method shows superior generalization across various lighting conditions, and a new m...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05249
• PDF: https://arxiv.org/pdf/2601.05249
• Project Page: https://ntuneillee.github.io/research/rl-awb/

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#ReinforcementLearning #ComputerVision #ImageProcessing #AutoWhiteBalance #LowLightImaging

✨Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset

📝 Summary:
This paper introduces IMDD-1M, a large dataset of 1 million industrial defect image-text pairs. It enables training a vision-language foundation model tailored for industrial use. This model achieves comparable performance with less data for specialized tasks, promoting data-efficient quality ins...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160

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#IndustrialAI #VisionLanguageModel #DefectDetection #MultimodalAI #ComputerVision

✨ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting

📝 Summary:
ProFuse enhances open-vocabulary 3DGS understanding via an efficient, context-aware framework. It uses a pre-registration phase to fuse semantic features onto Gaussians for cross-view coherence, completing semantic attachment twice as fast as SOTA.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04754
• PDF: https://arxiv.org/pdf/2601.04754
• Project Page: https://chiou1203.github.io/ProFuse/
• Github: https://chiou1203.github.io/ProFuse/

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#3DGaussianSplatting #ComputerVision #OpenVocabulary #3DReconstruction #DeepLearning

✨RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

📝 Summary:
RL-AWB is a novel framework for nighttime auto white balance. It combines statistical methods with deep reinforcement learning, mimicking expert tuning to improve color constancy in low-light scenes. The method shows superior generalization across various lighting conditions and includes a new mu...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05249
• PDF: https://arxiv.org/pdf/2601.05249
• Project Page: https://ntuneillee.github.io/research/rl-awb/
• Github: https://github.com/BrianChen1120/RL-AWB

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#ReinforcementLearning #DeepLearning #ComputerVision #ImageProcessing #AWB

✨RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation

📝 Summary:
Collecting diverse robot manipulation data is challenging. This paper introduces visual identity prompting, using exemplar images to guide diffusion models for generating multi-view, temporally coherent data. This augmented data improves robot policy performance in both simulation and real-world ...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05241
• PDF: https://arxiv.org/pdf/2601.05241
• Project Page: https://robovip.github.io/RoboVIP/
• Github: https://robovip.github.io/RoboVIP/

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#Robotics #AI #GenerativeAI #ComputerVision #MachineLearning