2406.08332] UDON: Universal Dynamic Online distillatioN for generic image representations Skip to main content We gratefully

2208.08349] Open Long-Tailed Recognition in a Dynamic World Skip to main content We gratefully

2309.04581] Dynamic Mesh-Aware Radiance Fields Happy Open Access Week from arXiv! YOU make open access possible! Tell

2406.08332] UDON: Universal Dynamic Online distillatioN for generic image representations Skip to main content We gratefully

2501.10021] X-Dyna: Expressive Dynamic Human Image Animation Skip to main content We gratefully acknowledge support from the

2412.04463] MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos Happy Open Access

2405.14868] Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis Skip to main content We gratefully acknowledge support

2501.10021] X-Dyna: Expressive Dynamic Human Image Animation Skip to main content We gratefully acknowledge support from the

2408.11375] Bootstrapping Dynamic APSP via Sparsification Skip to main content We gratefully acknowledge support from the

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2302.06548] Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning Skip to main content

2311.06402] A Dynamic Shortest Paths Toolbox: Low-Congestion Vertex Sparsifiers and their Applications Skip to

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2304.01159] DribbleBot: Dynamic Legged Manipulation in the Wild Skip to main content We gratefully

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2404.11483] AgentKit: Structured LLM Reasoning with Dynamic Graphs Skip to main content We gratefully acknowledge support from the

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2312.03029] HHAvatar: Gaussian Head Avatar with Dynamic Hairs Skip to main content We gratefully acknowledge support from the

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2002.09127] Learning Dynamic Belief Graphs to Generalize on Text-Based Games Skip to main content

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2504.17788] Dynamic Camera Poses and Where to Find Them Skip to main

2302.01607] dynamite: An R Package for Dynamic Multivariate Panel Models Happy Open Access Week from arXiv! YOU make

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2008.07012] DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping Skip to main content We gratefully acknowledge support from

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2010.00560] Dynamic Facial Asset and Rig Generation from a Single Scan Happy Open Access

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2112.06904] HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture Skip to

PDF HTML (experimental) Abstract: We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular

the World, by Haiwen Feng and 7 other authors View PDF HTML (experimental) Abstract: Dynamic 3D reconstruction and

world of static images, allowing models to reason about the dynamic context behind visual scenes

challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while

object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code is released

challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while

object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code is released

relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination

Abstract: This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K

Omega}(n^{3/2})$ that applied to all previous approaches for partially-dynamic SSSP [STOC'14, SODA

and Konrad Schindler and Iro Armeni View PDF HTML (experimental) Abstract: Research into dynamic 3D scene understanding has

while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a

densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in

4 reference in just 27% of the comparison. VisIT-Bench is dynamic to participate

based on customizable first-order logic (FOL) for an urban-like environment with multiple dynamic agents. LogiCity models diverse

regarding the latent denoising steps, where bilateral connections can be established upon. Specifically, we propose dynamic diffuser, a meta

computes the flow via a sequence of $m^{1+o(1)}$ dynamic min-ratio cut problems

relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination

Abstract: This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K

candidates for navigating challenging environments because of their agile and dynamic designs. This paper presents

environment, one's motion may also be influenced by the motion and dynamic movements of others

densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in

turn to language as a source of supervision for dynamic traffic scene generation. Our

and imitation learning to boost the controller's performance in dynamic environments. At the

with unprecedented fidelity, and various recent works have extended NeRF to handle dynamic scenes. A common

the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from

and images. However, the challenging problem of text-to-4D dynamic 3D scene generation with

emphasize the importance of type consistency when using JSON to exchange dynamic data, and illustrate

PDF HTML (experimental) Abstract: Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision

to-image and text-to-video models to generate dynamic 3D scenes. However, current

the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from

emphasize the importance of type consistency when using JSON to exchange dynamic data, and illustrate

Jin and 5 other authors View PDF HTML (experimental) Abstract: Learning to understand dynamic 3D scenes from imagery

for camera control learning with a curated dataset of 20K diverse, dynamic videos with stationary cameras

mixture flow matching (GMFlow) model: instead of predicting the mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters

each image an input-conditional token (vector). Compared to CoOp's static prompts, our dynamic prompts adapt to

descents, sitting and standing from chairs and benches, as well as other dynamic whole-body skills-all

of cases, even when prompted with privacy-enhancing instructions. We also demonstrate the dynamic nature of PrivacyLens

experiment results reveal their effectiveness and reliability in complex and dynamic environments. Subjects: Robotics (cs

Our approach yields state of the art view synthesis techniques, operates on low dynamic range imagery, and

the system to efficiently and robustly collect trolleys in dynamic and complex