Teaser image to MCML at CVPR 2024

MCML at CVPR 2024

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR 2024). Seattle, Washington, United States, June 17-21, 2024

We are happy to announce that MCML researchers are represented with 37 papers at CVPR 2024:

S. Aneja, J. Thies, A. Dai and M. Nießner.
FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models.
L. Bastian, Y. Xie, N. Navab and Z. Lähner.
Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching.
M. Brahimi, B. Haefner, Z. Ye, B. Goldluecke and D. Cremers.
Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo.
A.-Q. Cao, A. Dai and R. de Charette.
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness.
D. Cao, M. Eisenberger, N. E. Amrani, D. Cremers and F. Bernard.
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation.
W. Cao, C. Luo, B. Zhang, M. Nießner and J. Tang.
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking.
Y. Chen, Y. Di, G. Zhai, F. Manhardt, C. Zhang, R. Zhang, F. Tombari, N. Navab and B. Busam.
SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation.
D. Z. Chen, H. Li, H.-Y. Lee, S. Tulyakov and M. Nießner.
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors.
C. Diller and A. Dai.
CG-HOI: Contact-Guided 3D Human-Object Interaction Generation.
C. Diller, T. Funkhouser and A. Dai.
FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations.
V. Ehm, M. Gao, P. Roetzer, M. Eisenberger, D. Cremers and F. Bernard.
Partial-to-Partial Shape Matching with Geometric Consistency.
M. Ghahremani, M. Khateri, B. Jian, B. Wiestler, E. Adeli and C. Wachinger.
H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration.
S. Giebenhain, T. Kirschstein, M. Georgopoulos, M. Rünz, L. Agapito and M. Nießner.
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos.
K. Han, D. Muhle, F. Wimbauer and D. Cremers.
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation.
L. Höllein, A. Božič, N. Müller, D. Novotny, H.-Y. Tseng, C. Richardt, M. Zollhöfer and M. Nießner.
ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models.
J. Huang, H. Yu, K.-T. Yu, N. Navab, S. Ilic and B. Busam.
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images.
H. Jung, G. Zhai, S.-C. Wu, P. Ruhkamp, H. Schieber, G. Rizzoli, P. Wang, H. Zhao, L. Garattoni, S. Meier, D. Roth, N. Navab and B. Busam.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios.
T. Kirschstein, S. Giebenhain and M. Nießner.
DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars.
P. Kocsis, J. Philip, K. Sunkavalli, M. Nießner and Y. Hold-Geoffroy.
LightIt: Illumination Modeling and Control for Diffusion Models.
P. Kocsis, V. Sitzmann and M. Nießner.
Intrinsic Image Diffusion for Indoor Single-view Material Estimation.
L. Li and A. Dai.
GenZI: Zero-Shot 3D Human-Scene Interaction Generation.
H. Li, C. Shen, P. Torr, V. Tresp and J. Gu.
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation.
arXiv. GitHub.
N. Müller, K. Schwarz, B. Rössle, L. Porzi, S. R. Bulò, M. Nießner and P. Kontschieder.
MultiDiff: Consistent Novel View Synthesis from a Single Image.
S. Niedermayr, J. Stumpfegger and R. Westermann.
Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis.
S. Qian, T. Kirschstein, L. Schoneveld, D. Davoli, S. Giebenhain and M. Nießner.
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians.
C. Reich, B. Debnath, D. Patel, T. Prangemeier, D. Cremers and S. Chakradhar.
Deep Video Codec Control for Vision Models.
C. Reich, O. Hahn, D. Cremers, S. Roth and B. Debnath.
A Perspective on Deep Vision Performance with Standard Image and Video Codecs.
D. Rozenberszki, O. Litany and A. Dai.
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes.
Y. Siddiqui, A. Alliegro, A. Artemov, T. Tommasi, D. Sirigatti, V. Rosov, A. Dai and M. Nießner.
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers.
J. Tang, A. Dai, Y. Nie, L. Markhasin, J. Thies and M. Niessner.
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking.
J. Tang, Y. Nie, L. Markhasin, A. Dai, J. Thies and M. Nießner.
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis.
A. Toker, M. Eisenberger, D. Cremers and L. Leal-Taixé.
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation.
S. Weber, T. Dagès, M. Gao and D. Cremers.
Finsler-Laplace-Beltrami Operators with Application to Shape Analysis.
S. Weber, B. Zöngür, N. Araslanov and D. Cremers.
Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball.
F. Wimbauer, B. Wu, E. Schoenfeld, X. Dai, J. Hou, Z. He, A. Sanakoyeu, P. Zhang, S. Tsai, J. Kohler, C. Rupprecht, D. Cremers, P. Vajda and J. Wang.
Cache Me if You Can: Accelerating Diffusion Models through Block Caching.
H. Wu, X. Zuo, S. Leutenegger, O. Litany, K. Schindler and S. Huang.
Dynamic LiDAR Re-simulation using Compositional Neural Fields.
L. Yang, L. Hoyer, M. Weber, T. Fischer, D. Dai, L. Leal-Taixé, D. Cremers, M. Pollefeys and L. Van Gool.
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation. (Workshop paper).



Link to MCML at NAACL 2024


MCML at NAACL 2024

We are happy to announce that MCML researchers are represented at the 2024 Annual Conference of the North American Chapter of the Association for Computational …

Link to MCML at CHI 2024


MCML at CHI 2024

We are happy to announce that MCML researchers are represented at the CHI 2024 conference on Human Factors in Computing Systems.

Link to MCML at AISTATS 2024



We are happy to announce that MCML researchers are represented at the 27th International Conference on Artificial Intelligence and Statistics.

Link to MCML at ICLR 2024


MCML at ICLR 2024

We are happy to announce that MCML researchers are represented at the 12th International Conference on Learning Representations.

Link to MCML at PAKDD 2024


MCML at PAKDD 2024

We are happy to announce that MCML researchers are represented at the 28th PAKDD.