REVIEW 13 cited by
I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
read the original abstract
Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including control precision and the neglect of the control for subject motion dynamics. In this work, we propose I2VControl-Camera, a novel camera control method that significantly enhances controllability while providing adjustability over the strength of subject motion. To improve control precision, we employ point trajectory in the camera coordinate system instead of only extrinsic matrix information as our control signal. To accurately control and adjust the strength of subject motion, we explicitly model the higher-order components of the video trajectory expansion, not merely the linear terms, and design an operator that effectively represents the motion strength. We use an adapter architecture that is independent of the base model structure. Experiments on static and dynamic scenes show that our framework outperformances previous methods both quantitatively and qualitatively. The project page is: https://wanquanf.github.io/I2VControlCamera .
Forward citations
Cited by 13 Pith papers
-
Probing into Camera Control of Video Models
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.
-
OmniCamera: A Unified Framework for Multi-task Video Generation with Arbitrary Camera Control
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.
-
NavWM: A Unified Navigation World Model for Foresight-Driven Planning
NavWM unifies latent world tokens and anchor-based multimodal trajectory forecasting into a closed-loop planner that improves future state generation and zero-shot navigation.
-
TriMotion: Modality-Agnostic Camera Control for Video Generation
TriMotion is a modality-agnostic framework that maps video, pose, and text descriptions of the same camera trajectory into a shared motion embedding space, trained with a new triplet dataset and latent consistency obj...
-
Latent Spatial Memory for Video World Models
Mirage stores and queries 3D scene information in diffusion latent space via depth-guided lifting and warping, yielding 10.57× faster generation and 55× smaller memory than explicit RGB point-cloud baselines while rea...
-
RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control
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.
-
INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling
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...
-
SymphoMotion: Joint Control of Camera Motion and Object Dynamics for Coherent Video Generation
SymphoMotion jointly controls camera trajectories and depth-aware object dynamics inside one video diffusion model, supported by the new RealCOD-25K real-world paired-motion dataset.
-
UCM: Unified Modeling of Camera Control and Memory with Time-aware Positional Encoding Warping for World Models
A video-generation world model that warps positional encodings of memory frames to target viewpoints achieves state-of-the-art long-term consistency and camera control.
-
CustomX: Unified Character, Action, and Scene Customization in Video World Models
AniX generates controllable videos of a user-supplied character performing typed actions inside a user-supplied 3D scene by fine-tuning a pre-trained video generator on small locomotion datasets.
-
PostCam: Camera-Controllable Novel-View Video Generation with Query-Shared Cross-Attention
PostCam generates new videos from a reference video along user-specified camera trajectories using a query-shared cross-attention that fuses pose data and rendered frames, improving control precision and detail preservation.
-
PE-Field 4D: Video Generation Models as Canvas
Warping reference tokens' positional encodings into the target view, with depth offsets and frame-level compression fixes, improves geometry-aware camera control in video diffusion transformers.
-
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Physical intelligence needs an embodied brain that reasons over interventions and emits capability requests, grounded by a physical harness and shared experience contracts rather than direct actuator policies.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.