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Computer Vision Papers·2026
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PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception

Yana Wei, Hongbo Peng +15

Provides a rubric-based benchmark that converts dense image captions into instance-specific atomic checks (Must-Right and Easy-Wrong) and a gated scoring rule, aiming to expose perceptual brittleness and better align multimodal model evaluation with human judgment.

#evaluation#multimodal#vision#image#paper+2
Hugging Face
AI Image·2026
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Sun Direction LoRA (Flux2Klein 9B)

eric-venti-seeds

Applies or repositions directional sunlight in outdoor images by using a LoRA trained for Flux2Klein 9B to match a reference sun elevation and rotation. Workflow uses an overcast intermediate and a sphere (ball) reference; includes a ComfyUI node and Blender scene for rendering the reference.

#ai-image#image#huggingface#diffusers#ai-demos
Hugging Face
AI Dataset·2026
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FineBooks BHL IMPACT Ground Truth

finebooks (Hugging Face), IMPACT Centre of Competence +1

Provides 2,165 historical natural-history page scans paired with ~99.95% expert transcriptions and pixel-aligned PAGE XML layout ground truth for OCR and layout evaluation; multilingual (EN/FR/DE/LA), CC-BY 3.0.

#ocr#vision#multilingual#benchmark#huggingface+4
Hugging Face
AI Dataset·2026
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SenseNova Vision Corpus 50M

sensenova

Provides ~50M multimodal annotations organized for unified training across structured visual understanding, segmentation, dense geometric prediction, and multi-view reconstruction — released as task-specific JSONL files that reference original image assets rather than redistributing raw images.

#vision#multimodal#segmentation#depth#image+4
Hugging Face
AI Model·2026
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Giga-World-1

open-gigaai

Diffusion-based generative model for scene and video synthesis, providing full Diffusers checkpoints and scene LoRA for fast adaptation. Includes Stage‑1 nano (1.3B) and pro (5B) variants and modular transformer/VAE components.

#diffusers#huggingface#lora#pytorch#image+4
Hugging Face
AI Model·2026
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Krea-2 Depth ControlNet-LoRA

Patil

Depth-conditioned LoRA for Krea‑2 that extracts a depth map from any input image and generates new images preserving the original 3D structure and composition while changing content and style. Single 862MB LoRA, works with Krea‑2‑Raw and Krea‑2‑Turbo.

#depth#qwen#huggingface#ai-image#image+2
Computer Vision Papers·2026
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PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space

Sensen Gao, Zhaoqing Wang +4

Trains a single diffusion model that unifies 3D scene reconstruction and generative modeling by operating directly in pixel/rendered-image space. Supervises diffusion on rendered views and adds a geometry-perception loss from a pretrained 3D foundation model, reducing latent information loss and improving 3D fidelity.

#paper#vision#ai-image#image#depth+1
Hugging Face
AI Model·2026
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M87 (early-preview)

mgwr

Enhances KREA-2 Turbo image generations with an aesthetic LoRA trained on a curated 100-image dataset to add stronger composition, richer lighting, softer atmosphere and refined textures; trigger with --preview for art-directed, cinematic outputs in text-to-image pipelines.

#lora#huggingface#ai-image#image#AIGC
Hugging Face
AI Model·2026
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Krea 2 Identity Edit

conradlocke

Instruction-driven LoRA fine‑tune for identity‑preserving image edits: give an image plus a plain‑language instruction and it edits pose, outfit, objects or scene while keeping unasked content and subject likeness. Requires the ComfyUI‑Krea2Edit node pack; distributed under the Krea 2 Community License.

#ai-image#image#ai-image-demos#ai-demos#multimodal+1
Computer Vision Papers·2026
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Vision as Unified Multimodal Generation

Xiaoyang Han, Jianhua Li +15

Expresses diverse computer-vision tasks as instruction-driven text, image, or mixed generation from a single unified multimodal model, producing outputs for detection, segmentation, depth, pose, OCR and more. Trained on a converted SenseNova‑Vision instruction–response corpus and requires no task-specific prediction heads.

#multimodal#vision#paper#foundation-model#image+5
Computer Vision Papers·2026
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4D Human-Scene Reconstruction from Low-Overlap Captures

Minhyuk Hwang, Sangmin Kim +3·Seoul National UniversitySeoulRepublic of Korea

Reconstructs 4D dynamic human scenes from sparse, low-overlap multi-camera captures by decoupling background synthesis and human modeling. Synthesizes hundreds of camera-controlled background views with a video diffusion model, initializes deformable Gaussian humans via cross-view identity and triangulated keypoints, then applies motion-adaptive recursive enhancement to reduce artifacts.

#vision#video#ai-video#ai-image#image+2
AI Agent Papers·2026
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ABot-N1: Toward a General Visual Language Navigation Foundation Model

Ruiyan Gong, Yingnan Guo +38

Unifies high-level visual-language reasoning and low-level control for visual navigation by decoupling cognition and control: a slow vision-language reasoner produces pixel goals with explicit chain-of-thought, and a fast action expert converts those anchors into continuous waypoints for robust urban and indoor navigation.

#vision#multimodal#robotics#foundation-model#agent-skills+4
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