RayPE extends video DiT attention with Plucker coordinates and a gated reciprocal-product term to improve 3D consistency and camera controllability.
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CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
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Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video generation, limiting the expression of cinematic language and user control. To address this issue, we introduce CamCo, which allows fine-grained Camera pose Control for image-to-video generation. We equip a pre-trained image-to-video generator with accurately parameterized camera pose input using Pl\"ucker coordinates. To enhance 3D consistency in the videos produced, we integrate an epipolar attention module in each attention block that enforces epipolar constraints to the feature maps. Additionally, we fine-tune CamCo on real-world videos with camera poses estimated through structure-from-motion algorithms to better synthesize object motion. Our experiments show that CamCo significantly improves 3D consistency and camera control capabilities compared to previous models while effectively generating plausible object motion. Project page: https://ir1d.github.io/CamCo/
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Look-Before-Move is a framework that converts narrative intent into Semantic Observation Contracts, uses Monte Carlo Viewpoint Search for feasible viewpoints, and applies Semantic Trajectory Grounding for coherent camera motion in dynamic 3D story worlds.
A training-free method reformulates camera control as geometric displacement fields applied via differentiable latent resampling, enabling control and bias probing in video diffusion models.
PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
Reshoot-Anything trains a diffusion transformer on pseudo multi-view triplets created by cropping and warping monocular videos to achieve temporally consistent video reshooting with robust camera control on dynamic scenes.
A framework generates consistent multi-view scenes from one freehand sketch via a ~9k-sample dataset, Parallel Camera-Aware Attention Adapters, and Sparse Correspondence Supervision Loss, outperforming baselines in realism and consistency.
OmniCamera disentangles video content and camera motion for multi-task generation with arbitrary camera control via the OmniCAM hybrid dataset and Dual-level Curriculum Co-Training.
SparseCam4D achieves spatio-temporally consistent high-fidelity 4D reconstruction from sparse cameras via a Spatio-Temporal Distortion Field that corrects inconsistencies in generative observations.
A viewpoint-conditioned diffusion model generates stereo image pairs from monocular input in a canonical rectified space without using depth or explicit warping.
CamPVG is the first diffusion-based framework for generating geometrically consistent panoramic videos from camera pose inputs using a panoramic Plücker embedding and spherical epipolar attention module.
MemoBench curates 360 clips and an evaluation suite to test video models on recovering updated object states after disappear-and-reappear in changing environments.
Diffusion-based per-view harmonization for lighting-consistent object transfer between 3DGS scenes, using heterogeneous training data and final 3D consolidation.
StreetNVS presents a multi-sensor conditioned video diffusion framework for street-view novel view synthesis that outperforms baselines with sparse LiDAR and handles extreme out-of-trajectory paths on the Waymo dataset.
E³C is a video diffusion model that disentangles persistent 3D scene structure via point-cloud memory from human dynamics via ego-exo pose controls for improved egocentric video generation on the Nymeria dataset.
RealCam is a causal autoregressive model for real-time camera-controlled video-to-video generation, using cross-frame in-context teacher distillation and loop-closed data augmentation to achieve high fidelity and consistency.
PhyCo adds continuous physical control to video diffusion models via physics-supervised fine-tuning on a large simulation dataset and VLM-guided rewards, yielding measurable gains in physical realism on the Physics-IQ benchmark.
Lyra 2.0 produces persistent 3D-consistent video sequences for large explorable worlds by using per-frame geometry for information routing and self-augmented training to correct temporal drift.
INSPATIO-WORLD is a real-time framework for high-fidelity 4D scene generation and navigation from monocular videos via STAR architecture with implicit caching, explicit geometric constraints, and distribution-matching distillation.
Pixel-to-4D builds a dynamic 3D Gaussian representation from one image and samples object motion in a single forward pass to produce camera-controlled videos with claimed state-of-the-art quality and speed on KITTI, Waymo, RealEstate10K and DL3DV-10K.
A feed-forward video latent transformer that predicts time-varying 3D Gaussian primitives from one image to produce controllable 4D scenes with appearance, geometry, and motion.
ViewCrafter tames video diffusion models with point-based 3D guidance and iterative trajectory planning to produce high-fidelity novel views from single or sparse images.
CameraCtrl enables accurate camera pose control in video diffusion models through a trained plug-and-play module and dataset choices emphasizing diverse camera trajectories with matching appearance.
OptiWorld inserts a classical optimal-control layer that extracts a world state, plans an optimal trajectory on a geometric manifold under physical constraints, and renders the video conditioned on that trajectory.
SANA-WM is a 2.6B-parameter efficient world model that synthesizes minute-scale 720p videos with 6-DoF camera control, trained on 213K public clips in 15 days on 64 H100s and runnable on single GPUs at 36x higher throughput than prior open baselines.
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Lighting-Consistent Object Transfer Across Radiance Fields
Diffusion-based per-view harmonization for lighting-consistent object transfer between 3DGS scenes, using heterogeneous training data and final 3D consolidation.