Search Results for author:
Found 27 papers, 12 papers with code
I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
Without complex training and datasets, ThinkDiff effectively unleashes understanding, reasoning, and composing capabilities in diffusion models.
Wonderland: Navigating 3D Scenes from a Single Image
Specifically, we introduce a large-scale reconstruction model that uses latents from a video diffusion model to predict 3D Gaussian Splattings for the scenes in a feed-forward manner.
Omni-ID: Holistic Identity Representation Designed for Generative Tasks
Our approach uses a few-to-many identity reconstruction training paradigm, where a limited set of input images is used to reconstruct multiple target images of the same individual in various poses and expressions.
AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers
This suggested us to limit the injection of camera conditioning to a subset of the architecture to prevent interference with other video features, leading to a 4x reduction of training parameters, improved training speed, and 10% higher visual quality.
FastPCI: Motion-Structure Guided Fast Point Cloud Frame Interpolation
Point cloud frame interpolation is a challenging task that involves accurate scene flow estimation across frames and maintaining the geometry structure.
TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks
Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy.
VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control
Recently, new methods demonstrate the ability to generate videos with controllable camera poses these techniques leverage pre-trained U-Net-based diffusion models that explicitly disentangle spatial and temporal generation.
GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering
With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing the rendering speed by up to 39%.
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.
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos.
Dr$^2$Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision.
Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
We use two coefficients on either type of residual connections respectively and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision.
GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering
With the aid of a frequency-modulated loss GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing the rendering speed by up to 39%.
SPAD: Spatially Aware Multi-View Diffusers
We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images.
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors.
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Although acquired extensive knowledge of visual concepts, it is non-trivial to exploit knowledge from these VL models to the task of semantic segmentation, as they are usually trained at an image level.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+3
LLM as A Robotic Brain: Unifying Egocentric Memory and Control
In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control.
Exploring Open-Vocabulary Semantic Segmentation from CLIP Vision Encoder Distillation Only
Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+3
Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding
Pretrained on a large number of widely available images, significant gains of PViT are observed in the tasks of 3D point cloud classification, part segmentation, and semantic segmentation on ScanObjectNN, ShapeNetPart, and S3DIS, respectively.
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Ranked #3 on
3D Semantic Segmentation
on OpenTrench3D
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #36 on
3D Part Segmentation
on ShapeNet-Part
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
SGAS: Sequential Greedy Architecture Search
Architecture design has become a crucial component of successful deep learning.
Ranked #4 on
Node Classification
on PPI
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #5 on
3D Semantic Segmentation
on PartNet
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
