12 accepted papers
While class-agnostic object counting has recently evolved from image-exemplar to language-guided paradigms, existing methods are limited by text polysemy and the lack of prompts in audio-sensing scenarios. To overcome these challenges, we introduce a sound-centric counting paradigm, enabling models
We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically, we design a domain-specific language (DSL) to hierarchically represent building structures as a program, which can be e…
"Given a single chair image, could we wake it up by reconstructing its 3D shape and skeleton, as well as animating its plausible articulations and motions, similar to that of human modeling? It is a new problem that not only goes beyond image-based object reconstruction but also involves articulated…
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals. Events, on the other hand, have their unique challenges: rather than capturing static body postures, the event signals…
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applic…
Crowd counting from unconstrained and congested scenes is an important task in computer vision. Its main difficulties stem from large scale/density variation and prone to over-fitting. This paper presents a novel end-to-end stochastic multi-scale aggregation network (SMANet) which carefully addresse…
Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al. However, existing state-of-the-art approaches often generate insufficiently stylized results under challenging cases. We believe a fundamental reason is that these approaches try…
This paper presents the first approach for simultaneously recovering the 3D shape of both the wavy water surface and the moving underwater scene. A portable camera array system is constructed, which captures the scene from multiple viewpoints above the water. The correspondences across these cameras…
Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractica…
3D reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision. Unlike previous approaches that reconstruct each surface point independently and often return noisy depth maps, we propose a novel global optimization-based approach that recovers both depths and norma…
Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction. Under the assumption that the rays refract only twice when traveling through the object, we present the first approach to simultaneously reconstructing the 3D positions and normals of the…
Extracting environment mattes using existing approaches often requires either thousands of captured images or a long processing time, or both. In this paper, we propose a novel approach to capturing and extracting the matte of a real scene effectively and efficiently. Grown out of the traditional fr…