Gonzalo Martin Garcia

PhD student in Computer Vision at RWTH Aachen University, advised by Prof. Bastian Leibe.
I work on geometry estimation and diffusion models.

Publications

arXiv 2026

ARROW: Arbitrary Reconstruction and Tracking of 4D Observations in the Wild

Ilya Fradlin, Christian Schmidt, Jens Piekenbrinck, Karim Knaebel, Gonzalo Martin Garcia, Bastian Leibe

Joint 3D reconstruction and point tracking from arbitrary image sets, across viewpoints and time.

Paper Project Code —

NeurIPS 2026

SurGe: Improved Surface Geometry in Point Maps

Karim Knaebel, Gonzalo Martin Garcia, Christian Schmidt, Ilya Fradlin, Lucas Nunes, Daan de Geus, Bastian Leibe

Improved local surface geometry in point maps via a Neighborhood Attention Decoder (NAD).

Paper Project Code —

WACV 2025 Oral

Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think

Gonzalo Martin Garcia, Karim Knaebel, Christian Schmidt, Daan de Geus, Alexander Hermans, Bastian Leibe

Repurposing pretrained text-to-image diffusion models as single-step geometry regressors.

Paper Project Code —

Dynamic scenes may be captured by a moving camera, multiple video streams, or images taken at different times. These observations reveal complementary aspects of scene geometry and motion, yet bringing them together requires establishing correspondence across viewpoints, capture times, and visibility changes. We introduce ARROW, a feed-forward model that unifies 3D reconstruction and 3D point tracking from arbitrary image sets. At its core is a novel order-invariant querying approach, which allows the association of queries with observations across arbitrary inputs. We show that exposing the model to more diverse sets of inputs during training results in improved task performance. Moreover, the resulting model is capable of generalization to a wider range of tasks including multi-view tracking. Trained with this strategy, ARROW establishes a new state of the art in 3D tracking on WorldTrack and TAPVid-3D and outperforms dedicated multi-view trackers on an adapted RGB-only MVTracker benchmark, while remaining competitive across 3D reconstruction tasks. Code and weights are publicly available. 

@article{fradlin2026arrow,
    title         = {{ARROW}: Arbitrary Reconstruction and Tracking of 4D Observations in the Wild},
    author        = {Fradlin, Ilya and Schmidt, Christian and Piekenbrinck, Jens and Knaebel, Karim and Martin Garcia, Gonzalo and Leibe, Bastian},
    year          = 2026,
    journal       = {arXiv preprint arXiv:2610.01314},
}
ARROW’s 4D reconstruction and the car’s colored motion track

Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface geometry, which is clearly visible qualitatively but only weakly reflected in common metrics. To make these errors more explicit in evaluation, we introduce a point map normal metric that evaluates the local surface orientation induced by neighboring 3D predictions. To reduce these errors, we propose two complementary components: a point gradient matching loss that supervises depth-normalized 3D finite differences, and a Neighborhood Attention Decoder (NAD) that progressively upsamples features and uses Neighborhood Attention for local feature mixing. Across eight zero-shot monocular geometry benchmarks, our model, SurGe, achieves the best average rank for global point map AbsRel and consistently improves local point map and point map normal evaluations. 

@inproceedings{knaebel2026surge,
    title     = {{SurGe}: Improved Surface Geometry in Point Maps},
    author    = {Knaebel, Karim and Martin Garcia, Gonzalo and Schmidt, Christian and Fradlin, Ilya and Nunes, Lucas and de Geus, Daan and Leibe, Bastian},
    year      = 2026,
    booktitle = {Advances in Neural Information Processing Systems},
}
Large preview image for SurGe

Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state-of-the-art results, high computational demands due to multi-step inference limited its use in many scenarios. In this paper, we show that the perceived inefficiency was caused by a flaw in the inference pipeline that has so far gone unnoticed. The fixed model performs comparably to the best previously reported configuration while being more than 200× faster. To optimize for downstream task performance, we perform end-to-end fine-tuning on top of the single-step model with task-specific losses and get a deterministic model that outperforms all other diffusion-based depth and normal estimation models on common zero-shot benchmarks. We surprisingly find that this fine-tuning protocol also works directly on Stable Diffusion and achieves comparable performance to current state-of-the-art diffusion-based depth and normal estimation models, calling into question some of the conclusions drawn from prior works. 

@inproceedings{martingarcia2024diffusione2eft,
    title     = {Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think},
    author    = {Martin Garcia, Gonzalo and Knaebel, Karim and Schmidt, Christian and de Geus, Daan and Hermans, Alexander and Leibe, Bastian},
    year      = 2025,
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
}
Large preview image for Fine-Tuning Image-Conditional Diffusion Models