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Found 31 papers, 5 papers with code
CoProSketch: Controllable and Progressive Sketch Generation with Diffusion Model
We propose a novel framework CoProSketch, providing prominent controllability and details for sketch generation with diffusion models.
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation.
LAM: Large Avatar Model for One-shot Animatable Gaussian Head
The centerpiece of our framework is the canonical Gaussian attributes generator, which utilizes FLAME canonical points as queries.
MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation
In the generation stage, we adopt a Diffusion Transformer (DiT) model to generate PBR materials, where both the specially designed multi-branch DiT and reference-based DiT blocks adopt a global attention mechanism to promote feature interaction and fusion between different views, thereby improving multi-view consistency.
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow
Generating motion sequences conforming to a target style while adhering to the given content prompts requires accommodating both the content and style.
AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction
Generating animatable human avatars from a single image is essential for various digital human modeling applications.
MVImgNet2.0: A Larger-scale Dataset of Multi-view Images
This paper constructs the MVImgNet2. 0 dataset that expands MVImgNet into a total of ~520k objects and 515 categories, which derives a 3D dataset with a larger scale that is more comparable to ones in the 2D domain.
LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning
For captioning, we finetune a large language model with the language-informative motion features to develop a strong motion captioning model.
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors.
HIVE: HIerarchical Volume Encoding for Neural Implicit Surface Reconstruction
To better represent 3D shapes, we introduce a volume encoding to explicitly encode the spatial information.
StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
The effectiveness of StableNormal is demonstrated through competitive performance in standard datasets such as DIODE-indoor, iBims, ScannetV2 and NYUv2, and also in various downstream tasks, such as surface reconstruction and normal enhancement.
GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation
To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework that leverages 3D Gaussian representation to not only capture explicit shapes but also enable the simulated continuum to render object masks as 2D shape surrogates during training.
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes
In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Images from video generative models are more suitable for multi-view generation because the underlying network architecture that generates them employs a temporal module to enforce frame consistency.
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF.
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images
This approach treats the query points for implicit field learning as a noisy point cloud for iterative denoising allowing for their dynamic adaptation to the target object shape.
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors
The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker.
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing
Recently, Neural Radiance Fields (NeRF) has exhibited significant success in novel view synthesis, surface reconstruction, etc.
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Panoramic image enables deeper understanding and more holistic perception of $360^\circ$ surrounding environment, which can naturally encode enriched scene context information compared to standard perspective image.
3D Former: Monocular Scene Reconstruction with 3D SDF Transformers
In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation.
Dense RGB SLAM with Neural Implicit Maps
To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder.
${S}^{2}$Net: Accurate Panorama Depth Estimation on Spherical Surface
In this paper, we propose an end-to-end deep network for monocular panorama depth estimation on a unit spherical surface.
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated.
AR Mapping: Accurate and Efficient Mapping for Augmented Reality
Secondly, we propose an AR mapping pipeline which takes the input from the scanning device and produces accurate AR Maps.
DRO: Deep Recurrent Optimizer for Video to Depth
There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques.
UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation
Besides, we also designed a more effective fusion module for our fusion scheme.
Ranked #1 on
Depth Estimation
on Matterport3D
ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion
Our framework consists of steps of solving the feature `dropout' problem when indistinctive structures, noise or large image distortion exists, and of rapidly recognizing and joining common features located in different subsequences.
