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Found 16 papers, 4 papers with code
Towards A Better Metric for Text-to-Video Generation
Experiments on the TVGE dataset demonstrate the superiority of the proposed T2VScore on offering a better metric for text-to-video generation.
ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors
Previous methods using 2D diffusion priors to optimize neural radiance fields for generating room-scale scenes have shown unsatisfactory quality.
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape.
ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy
Specifically, to reconstruct the entire colon in a piecewise manner, our ColonNeRF introduces a region division and integration module, effectively reducing shape dissimilarity and ensuring geometric consistency in each segment.
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model
To enhance the guidance ability of X-Adapter, we employ a null-text training strategy for the upgraded model.
MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes
MD-Splatting builds on recent advances in Gaussian splatting, a method that learns the properties of a large number of Gaussians for state-of-the-art and fast novel view synthesis.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion.
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.
Instant3D: Instant Text-to-3D Generation
Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network.
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
To overcome this, we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation, where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field.
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.
Ranked #2 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video
Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints.
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i. e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields.
