Papers with Code 아카이브

3D Reconstruction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 7
평가표: 10
보존된 평가 기록: 44

3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.

Image: Gwak et al

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/samiragarwala/PlaneFormers

공식 코드

PlaneFormers: From Sparse View Planes to 3D Reconstruction

https://github.com/sanjaysaha1311/temporal-deepfake-segmentation

공식 코드

Undercover Deepfakes: Detecting Fake Segments in Videos

https://github.com/sascha-kirch/VoloGAN

공식 코드

VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data

https://github.com/sbrunoberenguel/omniscv

공식 코드

OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision

https://github.com/sbsws88/tfs_nerf

공식 코드

TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene

https://github.com/sccsok/Locate-and-Verify

공식 코드

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

https://github.com/sclbd/deepfakebench

공식 코드

DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

https://github.com/scu-zjz/forensichub

공식 코드

ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization

https://github.com/seannz/svr

공식 코드

Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI

https://github.com/sebulo/PuTT

공식 코드

Coarse-To-Fine Tensor Trains for Compact Visual Representations

https://github.com/sercharles/cn-rma

공식 코드

CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Images

https://github.com/sergibot/fakefacessurvey

공식 코드

Testing Human Ability To Detect Deepfake Images of Human Faces

https://github.com/sh1newu/advmark

공식 코드

Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics

https://github.com/sh1newu/sepmark

공식 코드

SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection

https://github.com/shaharlutatipersonal/ocd

공식 코드

OCD: Learning to Overfit with Conditional Diffusion Models

https://github.com/shahroztariq/CLRNet

공식 코드

A Convolutional LSTM based Residual Network for Deepfake Video Detection

https://github.com/shanface33/gpt4mf_ub

공식 코드

Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics

https://github.com/shichengchen/multiviewdataset

공식 코드

Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation

https://github.com/shimmer-ghq/deepfidelity

공식 코드

DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection

https://github.com/shivangi-aneja/TAFIM

공식 코드

TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations

https://github.com/shurans/sscnet

공식 코드

Semantic Scene Completion from a Single Depth Image

https://github.com/sihwa-park/nebla

공식 코드

NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs

https://github.com/silence-tang/gaussianactor

공식 코드

3D$^2$-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling

https://github.com/simongiebenhain/air-nets

공식 코드

AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations

https://github.com/simonwebertum/poba

공식 코드

Power Bundle Adjustment for Large-Scale 3D Reconstruction

https://github.com/sjtuxcx/ITES

공식 코드

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

https://github.com/skhu101/consistentnerf

공식 코드

ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis

https://github.com/skjack/diffusionfake

공식 코드

DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion

https://github.com/skrantidatta/lipinc

공식 코드

Exposing Lip-syncing Deepfakes from Mouth Inconsistencies

https://github.com/slundberg/shap

공식 코드

Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations

https://github.com/smartroboticslab/vimid

공식 코드

Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation

https://github.com/sminchisescu-research/imar_vision_datasets_tools

공식 코드

Reconstructing Three-Dimensional Models of Interacting Humans

https://github.com/snldmt/SPARS3R

공식 코드

SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction

https://github.com/snuvclab/chupa

공식 코드

Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models

https://github.com/sohailahmedkhan/CLIPping-the-Deception

공식 코드

CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection

https://github.com/sohomd/3D2M-Dataset

공식 코드

3D2M Dataset: A 3-Dimension diverse Mesh Dataset

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.