Christopher Wewer PhD Student at Max Planck Institute for Informatics Short Biography PhD student at MPII Computer Vision and Machine Learning, advised by Dr. Jan E. Lenssen and Prof. Bernt Schiele. Research scientist internship at Meta Reality Labs. B.S. and M.S. degrees in Computer Science at RWTH Aachen University, advised by Prof. Cochez and Prof. Schaub.

Research Interests

News

• SceneTok: A Compressed, Diffusable Token Space for 3D Scenes CVPR 2026 Mohammad Asim, Christopher Wewer†, Jan Eric Lenssen Short Abstract: A scene tokenizer that encodes and decodes view-sets of a scene to and from a highly compressed, 1D token set representation that can be used for efficient latent scene diffusion. [Project Page] [Paper]

• Spatial Reasoners for Continuous Variables in Any Domain CodeML Workshop @ ICML 2025 Bart Pogodzinski, Christopher Wewer, Bernt Schiele, Jan Eric Lenssen Short Abstract: A software framework to bring spatial reasoning networks (SRMs) to other domains. Allows reasoning over sets of continuous variables [Project Page] [Paper]

• Spatial Reasoning with Denoising Models ICML 2025 Christopher Wewer, Bart Pogodzinski, Bernt Schiele, Jan Eric Lenssen Short Abstract: A framework to propagate belief over a set of continuous variables (e.g. image patches) with generative denoising models. Explores the amount of sequentialization and generation order. [Project Page] [Paper]

• MEt3R: Measuring Multi-View Consistency in Generated Images CVPR 2025 Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, Jan Eric Lenssen Short Abstract: A metric that measures 3D consistency between two generated images without requiring camera poses. It uses DUSt3R to perform reconstruction and compares projected DINO features. [Project Page] [Paper]

• Spurfies: Sparse Surface Reconstruction using Local Geometry Priors 3DV 2025 (Oral Presentation, Award Candidate) Kevin Raj, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen Short Abstract: A method leveraging synthetic data to learn local surface priors for surface reconstruction from few images. Can be applied to both bounded and unbounded scenes. [Project Page] [Paper]

• latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction ECCV 2024 Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen Short Abstract: A fast autoencoder that encodes image pairs into 3D variational feature Gaussians that model uncertainty individually for different locations in 3D space. Trained purely on videos. [Project Page] [Paper]

• Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction CVPR 2024 Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen Short Abstract: A two-stage approach for non-rigid reconstruction from monocular videos. A coarse point template is found first to act as regularization for a local 3D Gaussians representation. [Project Page] [Paper]

• Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation CVPR 2024 Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox Short Abstract: A denoising diffusion method on point clouds with features defining a radiance field. The disentangled representation allows for individual generation of shape and appearance. [Project Page] [Paper]

• SimNP: Learning Self-Similarity Priors between Neural Points ICCV 2023 Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen Short Abstract: A renderable neural point radiance field that learns category-level self-similarities from data by connecting coherent neural points to embeddings via optimized bipartite attention scores. [Project Page] [Paper]

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