Search Results for author:
Found 11 papers, 7 papers with code
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency.
Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
With the emergence of text-to-video (T2V) diffusion models, its temporal counterpart, motion customization, has not yet been well investigated.
VecFusion: Vector Font Generation with Diffusion
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.
LRM: Large Reconstruction Model for Single Image to 3D
We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds.
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
Neural Strokes: Stylized Line Drawing of 3D Shapes
We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours.
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.
Neural Contours: Learning to Draw Lines from 3D Shapes
This paper introduces a method for learning to generate line drawings from 3D models.
Neural Shape Parsers for Constructive Solid Geometry
We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.
Deep Part Induction from Articulated Object Pairs
In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.
CSGNet: Neural Shape Parser for Constructive Solid Geometry
In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.
