Search Results for author:
Found 17 papers, 6 papers with code
FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces
3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability.
Augmented Object Intelligence: Making the Analog World Interactable with XR-Objects
Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures.
InstructPipe: Building Visual Programming Pipelines with Human Instructions
Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.
Next Steps for Human-Centered Generative AI: A Technical Perspective
Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI).
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
PRIF: Primary Ray-based Implicit Function
We introduce a new implicit shape representation called Primary Ray-based Implicit Function (PRIF).
ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users
Recent advances have enabled automatic sound recognition systems for deaf and hard of hearing (DHH) users on mobile devices.
OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas
Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas.
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
A Log-Rectilinear Transformation for Foveated 360-degree Video Streaming
With the rapidly increasing resolutions of 360° cameras, head-mounted displays, and live-streaming services, streaming high-resolution panoramic videos over limited-bandwidth networks is becoming a critical challenge.
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality
Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map.
Depth And Camera Motion
Indoor Monocular Depth Estimation
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ORC Layout: Adaptive GUI Layout with OR-Constraints
We propose a novel approach for constraint-based graphical user interface (GUI) layout based on OR-constraints (ORC) in standard soft/hard linear constraint systems.
Human-Computer Interaction Graphics
Language-based Colorization of Scene Sketches
Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children.
LUCSS: Language-based User-customized Colourization of Scene Sketches
We introduce LUCSS, a language-based system for interactive col- orization of scene sketches, based on their semantic understanding.
SketchyScene: Richly-Annotated Scene Sketches
We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level.
