Search Results for author:
Found 20 papers, 6 papers with code
Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior
Once the UPM is learned to accurately reproduce the large-scale multi-view human images, we fine-tune the model with an in-the-wild video via inverse rendering to obtain a personalized photorealistic human avatar that can be faithfully animated to novel human motions and rendered from novel views.
Pippo: High-Resolution Multi-View Humans from a Single Image
Finally, we also introduce an improved metric to evaluate 3D consistency of multi-view generations, and show that Pippo outperforms existing works on multi-view human generation from a single image.
Fillerbuster: Multi-View Scene Completion for Casual Captures
Our solution is to train a generative model that can consume a large context of input frames while generating unknown target views and recovering image poses when desired.
SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation
Methods for image-to-video generation have achieved impressive, photo-realistic quality.
Realistic Evaluation of Model Merging for Compositional Generalization
Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance.
Pixel-Aligned Multi-View Generation with Depth Guided Decoder
During inference, we employ a rapid multi-view to 3D reconstruction approach, NeuS, to obtain coarse depth for the depth-truncated epipolar attention.
SPAD : Spatially Aware Multiview Diffusers
We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images.
AToM: Amortized Text-to-Mesh using 2D Diffusion
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously.
SPAD: Spatially Aware Multi-View Diffusers
We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images.
Virtual Pets: Animatable Animal Generation in 3D Scenes
Toward unlocking the potential of generative models in immersive 4D experiences, we introduce Virtual Pet, a novel pipeline to model realistic and diverse motions for target animal species within a 3D environment.
iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis
Our approach focuses on maximizing the reuse of visible pixels from the source image.
CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videos
Animating an object in 3D often requires an articulated structure, e. g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movements and surface deformations.
Invertible Neural Skinning
Next, we combine PIN with a differentiable LBS module to build an expressive and end-to-end Invertible Neural Skinning (INS) pipeline.
Building Scalable Video Understanding Benchmarks through Sports
Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed.
LaTeRF: Label and Text Driven Object Radiance Fields
In this paper we introduce LaTeRF, a method for extracting an object of interest from a scene given 2D images of the entire scene, known camera poses, a natural language description of the object, and a set of point-labels of object and non-object points in the input images.
Housekeep: Tidying Virtual Households using Commonsense Reasoning
Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house.
NarrationBot and InfoBot: A Hybrid System for Automated Video Description
To overcome the increasing gaps in video accessibility, we developed a hybrid system of two tools to 1) automatically generate descriptions for videos and 2) provide answers or additional descriptions in response to user queries on a video.
Contrast and Classify: Training Robust VQA Models
Recent Visual Question Answering (VQA) models have shown impressive performance on the VQA benchmark but remain sensitive to small linguistic variations in input questions.
Spatially Aware Multimodal Transformers for TextVQA
Further, each head in our multi-head self-attention layer focuses on a different subset of relations.
ICLR Reproducibility Challenge Report (Padam : Closing The Generalization Gap Of Adaptive Gradient Methods in Training Deep Neural Networks)
The authors try to address this problem by designing a new optimization algorithm that bridges the gap between the space of Adaptive Gradient algorithms and SGD with momentum.
