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Zhexiao Xiong's Projects

3ditscene icon 3ditscene

3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting

acvnet icon acvnet

[CVPR 2022] ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo Matching

anydoor icon anydoor

Official implementations for paper: Anydoor: zero-shot object-level image customization

attention-mask-control icon attention-mask-control

code for paper "Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models"

behindthescenes icon behindthescenes

Official implementation of the paper: Behind the Scenes: Density Fields for Single View Reconstruction (CVPR 2023)

boxdiff icon boxdiff

[ICCV 2023] BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion

bridgedepthflow icon bridgedepthflow

Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence, CVPR 2019

canonicalsg2im icon canonicalsg2im

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

clean-fid icon clean-fid

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

comogan icon comogan

CoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.

cvpr23_lfdm icon cvpr23_lfdm

The pytorch implementation of our CVPR 2023 paper "Conditional Image-to-Video Generation with Latent Flow Diffusion Models"

datasetdm icon datasetdm

[NeurIPS2023] DatasetDM:Synthesizing Data with Perception Annotations Using Diffusion Models

ddp icon ddp

diffusion for dense prediction

depth-anything icon depth-anything

[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

diffbev icon diffbev

Official PyTorch implementation for a conditional diffusion probability model in BEV perception

diffusion-spacetime-attn icon diffusion-spacetime-attn

Official implementation of the paper "Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis"

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