Search Results for author:
Found 23 papers, 8 papers with code
How to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse Topologies
Given a small number (3-5) of example frames showing the character in different poses with corresponding skeletal information, our model quickly infers a rig for that character that can generate images corresponding to new skeleton poses.
Visual Persona: Foundation Model for Full-Body Human Customization
We introduce Visual Persona, a foundation model for text-to-image full-body human customization that, given a single in-the-wild human image, generates diverse images of the individual guided by text descriptions.
VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation
To support diverse tasks and complex instructions, we employ a curriculum learning strategy: first aligning the MLLM and video diffusion model with large-scale instructional image editing data, followed by end-to-end fine-tuning on high-quality multitask video data.
Mean-Shift Distillation for Diffusion Mode Seeking
We present mean-shift distillation, a novel diffusion distillation technique that provides a provably good proxy for the gradient of the diffusion output distribution.
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization
In this work, we introduce a distillation method that combines variational score distillation and consistency distillation to achieve few-step video generation, maintaining both high quality and diversity.
Move-in-2D: 2D-Conditioned Human Motion Generation
Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal.
HARIVO: Harnessing Text-to-Image Models for Video Generation
We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models.
Progressive Autoregressive Video Diffusion Models
In this work, we introduce a more natural formulation of autoregressive long video generation by revisiting the noise level assumption in video diffusion models.
NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation
The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such as vector graphics.
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models
Specifically, for single-denoising-step pruning, we develop a novel ranking algorithm, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens, and a similarity-based recovery method to restore tokens for the convolution operation.
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.
Ranked #16 on
Video Super-Resolution
on Vid4 - 4x upscaling
(PSNR metric)
Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
To address the challenge of one-shot video motion customization, we propose Customize-A-Video that models the motion from a single reference video and adapts it to new subjects and scenes with both spatial and temporal varieties.
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method.
Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation
This paper proposes an image-vector dual diffusion model for generative layout design.
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.
