Important Computer Vision Papers for the Week from 27/01 to 01/02
Stay Updated with Recent Computer Vision Research
Every week, researchers from top research labs, companies, and universities publish exciting breakthroughs in diffusion models, vision language models, image editing and generation, video processing and generation, and image recognition.
This article provides a comprehensive overview of the most significant papers published in the Fifth week of January 2025, highlighting the latest research and advancements in computer vision.
Whether you’re a researcher, practitioner, or enthusiast, this article will provide valuable insights into the state-of-the-art techniques and tools in computer vision.
Table of Contents:
Diffusion Models
Vision Language Models
1. Diffusion Models
1.1. DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation
Recent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation.
We introduce DiffSplat, a novel 3D generative framework that natively generates 3D Gaussian splats by taming large-scale text-to-image diffusion models.
It differs from previous 3D generative models by effectively utilizing web-scale 2D priors while maintaining 3D consistency in a unified model. To bootstrap the training, a lightweight reconstruction model is proposed to instantly produce multi-view Gaussian splat grids for scalable dataset curation.
In conjunction with the regular diffusion loss on these grids, a 3D rendering loss is introduced to facilitate 3D coherence across arbitrary views.
The compatibility with image diffusion models enables seamless adaptions of numerous techniques for image generation to the 3D realm. Extensive experiments reveal the superiority of DiffSplat in text- and image-conditioned generation tasks and downstream applications.
Thorough ablation studies validate the efficacy of each critical design choice and provide insights into the underlying mechanism.
2. Vision Language Models
2.1. PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments.
While Vision-Language Models (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend physical phenomena remains extremely limited.
To close this gap, we introduce PhysBench, a comprehensive benchmark designed to evaluate VLMs’ physical world understanding capability across a diverse set of tasks.
PhysBench contains 10,002 entries of interleaved video-image-text data, categorized into four major domains: physical object properties, physical object relationships, physical scene understanding, and physics-based dynamics, further divided into 19 subclasses and 8 distinct capability dimensions.
Our extensive experiments, conducted on 75 representative VLMs, reveal that while these models excel in common-sense reasoning, they struggle with understanding the physical world — likely due to the absence of physical knowledge in their training data and the lack of embedded physical priors.
To tackle the shortfall, we introduce PhysAgent, a novel framework that combines the generalization strengths of VLMs with the specialized expertise of vision models, significantly enhancing VLMs’ physical understanding across a variety of tasks, including an 18.4% improvement on GPT-4o.
Furthermore, our results demonstrate that enhancing VLMs’ physical world understanding capabilities can help embodied agents such as MOKA. We believe that PhysBench and PhysAgent offer valuable insights and contribute to bridging the gap between VLMs and physical world understanding.
2.2. Visual Generation Without Guidance
Classifier-free guidance (CFG) has been a default technique in various visual generative models, yet it requires inference from both conditional and unconditional models during sampling.
We propose to build visual models that are free from guided sampling. The resulting algorithm, Guidance-Free Training (GFT), matches the performance of CFG while reducing sampling to a single model, halving the computational cost.
Unlike previous distillation-based approaches that rely on pretrained CFG networks, GFT enables training directly from scratch. GFT is simple to implement.
It retains the same maximum likelihood objective as CFG and differs mainly in the parameterization of conditional models. Implementing GFT requires only minimal modifications to existing codebases, as most design choices and hyperparameters are directly inherited from CFG.
Our extensive experiments across five distinct visual models demonstrate the effectiveness and versatility of GFT. Across domains of diffusion, autoregressive, and masked-prediction modeling, GFT consistently achieves comparable or even lower FID scores, with similar diversity-fidelity trade-offs compared with CFG baselines, all while being guidance-free.
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