✨Structured Causal Video Reasoning via Multi-Objective Alignment

📝 Summary:
This paper introduces Structured Event Facts for explicit causal video reasoning, moving beyond unstructured methods. It uses a multi-objective reinforcement learning pipeline to balance training goals, leading to Factum-4B. This model achieves reliable, stronger performance on complex temporal v...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04415
• PDF: https://arxiv.org/pdf/2604.04415

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#CausalAI #VideoReasoning #ReinforcementLearning #ComputerVision #AIResearch

✨3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis

📝 Summary:
3DTV is a feedforward network combining lightweight geometry and learning for real-time, robust sparse-view interpolation. It generates novel views efficiently without scene-specific optimization, making it practical for interactive applications.

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11211
• PDF: https://arxiv.org/pdf/2604.11211
• Project Page: https://stefanmschulz.github.io/3DTV_webpage/
• Github: https://github.com/StefanMSchulz/3DTV

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#ViewSynthesis #DeepLearning #ComputerVision #NeuralNetworks #RealTimeAI

✨ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video

📝 Summary:
ReconPhys is the first feedforward framework to jointly learn physical attribute estimation and 3D Gaussian Splatting reconstruction from a single video. It offers significantly faster inference and superior reconstruction quality for non-rigid objects compared to prior optimization-based methods...

🔹 Publication Date: Published on Apr 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07882
• PDF: https://arxiv.org/pdf/2604.07882
• Project Page: https://chuanshuogushi.github.io/ReconPhys/
• Github: https://chuanshuogushi.github.io/ReconPhys/

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#ComputerVision #3DReconstruction #GaussianSplatting #DeepLearning #AIResearch

✨VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

📝 Summary:
VEFX-Bench offers a large human-annotated video editing dataset and VEFX-Reward, a specialized model for quality assessment. This benchmark allows standardized comparison, showing current models struggle with instruction following and edit locality.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16272
• PDF: https://arxiv.org/pdf/2604.16272
• Project Page: https://xiangbogaobarry.github.io/VEFX-Bench/

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#VideoEditing #VFX #AI #ComputerVision #Benchmarks

✨NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

📝 Summary:
This paper overviews the NTIRE 2026 Challenge on Video Saliency Prediction. Participants developed automatic saliency map prediction for videos using a novel 2,000-video dataset with crowdsourced fixations. Over 20 teams submitted, and all challenge data is now publicly available.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14816
• PDF: https://arxiv.org/pdf/2604.14816
• Project Page: https://www.codabench.org/competitions/12842/
• Github: https://github.com/msu-video-group/NTIRE26_Saliency_Prediction

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#VideoSaliency #ComputerVision #NTIRE #MachineLearning #SaliencyPrediction

✨Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding

📝 Summary:
This paper improves vision-language models for compositional reasoning by using concreteness-based negative sample selection and a novel margin-based loss. Their framework, Slipform, achieves state-of-the-art accuracy on compositional benchmarks and cross-modal retrieval.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13313
• PDF: https://arxiv.org/pdf/2604.13313

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#VisionLanguage #DeepLearning #AIResearch #ComputerVision #NLP

✨CityRAG: Stepping Into a City via Spatially-Grounded Video Generation

📝 Summary:
CityRAG generates long-term, physically grounded video sequences that maintain environmental consistency and support complex navigation through real-world geography using geo-registered data as contex...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19741
• PDF: https://arxiv.org/pdf/2604.19741
• Project Page: https://cityrag.github.io/

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#VideoGeneration #GenerativeAI #SpatialAI #ComputerVision #UrbanSimulation

✨DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation

📝 Summary:
DeVI enables physically plausible dexterous robot control by leveraging text-conditioned synthetic videos through a hybrid tracking reward that combines 3D and 2D tracking for improved hand-object int...

🔹 Publication Date: Published on Apr 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20841
• PDF: https://arxiv.org/pdf/2604.20841
• Project Page: https://snuvclab.github.io/devi/
• Github: https://github.com/snuvclab/devi

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#Robotics #AI #ComputerVision #HumanRobotInteraction #DeepLearning

✨3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding

📝 Summary:
3D-VCD is a new inference-time framework that reduces hallucinations in 3D embodied agents. It constructs distorted 3D scene graphs and contrasts predictions to suppress ungrounded tokens. This improves reasoning on 3D benchmarks without retraining.

🔹 Publication Date: Published on Apr 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08645
• PDF: https://arxiv.org/pdf/2604.08645
• Project Page: https://plan-lab.github.io/projects/3d-vcd

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#3DLLM #EmbodiedAI #HallucinationMitigation #ComputerVision #AIResearch

✨FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

📝 Summary:
FlowAnchor stabilizes inversion-free video editing by addressing signal instability in high-dimensional latent spaces. It uses spatial-aware attention refinement and adaptive magnitude modulation to ensure precise localization and sufficient editing strength, leading to faithful and coherent vide...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22586
• PDF: https://arxiv.org/pdf/2604.22586

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#VideoEditing #DeepLearning #ComputerVision #GenerativeAI #AIResearch

✨Video Analysis and Generation via a Semantic Progress Function

📝 Summary:
Researchers developed a Semantic Progress Function to analyze and correct non-linear semantic evolution in generated media. This function identifies uneven pacing, enabling a linearization procedure that re-times sequences for smoother, more coherent transitions at a constant semantic rate.

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22554
• PDF: https://arxiv.org/pdf/2604.22554
• Project Page: https://sagipolaczek.github.io/semantic-progress-function/
• Github: https://github.com/SagiPolaczek/semantic-progress-function

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#VideoAI #GenerativeAI #ComputerVision #SemanticAnalysis #AIResearch