arXiv AI
Jul 1
Learning Video Dynamics with Predictive Differentiable Rendering
arXiv:2606. 31050v1 Announce Type: cross Abstract: How to accurately predict a high-fidelity future world?
InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics
Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos.
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arXiv AI
Jun 18
VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation
arXiv:2502. 07531v5 Announce Type: replace-cross Abstract: Controllable image-to-video (I2V) generation transforms a reference image into a coherent video guided by user-specified control signals.
Hugging Face Trending Papers
Jul 21
IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding.
arXiv AI
Jul 21
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
arXiv:2607. 17790v1 Announce Type: cross Abstract: Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment.
arXiv AI
Jun 2
Real2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion
arXiv:2606. 00299v1 Announce Type: cross Abstract: While Video Diffusion Models (VDMs) excel at synthesizing high-fidelity videos, enabling precise camera and scene control remains challenging.
arXiv AI
Jul 3
Learning 3D-Gaussian Simulators from RGB Videos
arXiv:2503. 24009v3 Announce Type: replace-cross Abstract: Realistic simulation is critical for applications ranging from robotics to animation.
Hugging Face Trending Papers
Jul 27
CameraAnything: Refilming Videos with Arbitrary Camera Control
We introduce CameraAnything, the first unified framework for camera controlled video editing that enables joint control of both intrinsic and extrinsic camera parameters. Existing approaches either rely on expensive 3D reconstruction to achieve full camera functionality or restrict editing to extrinsic parameter manipulation.
arXiv AI
Jul 24
RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
arXiv:2607. 20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction.
arXiv AI
Jul 7
MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
arXiv:2601. 22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.
arXiv AI
Jul 2
Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
arXiv AI
Jun 19
DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis
arXiv:2604. 13416v2 Announce Type: replace-cross Abstract: Advances in radiance fields have enabled photorealistic novel view synthesis.
arXiv AI
Jun 30
MotionAtlas: Detailed Region Captioning for Motion-Centric Videos
arXiv:2606. 29531v1 Announce Type: cross Abstract: We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a family of powerful Video-MLLMs.