ScenarioControl (ECCV 2026) enables end-to-end controllable generative simulation 🚗✨for autonomous driving, allowing users to generate diverse traffic scenarios with fine-grained actor control while preserving realistic scene dynamics. We have released the code, and we’re looking forward to seeing what the community builds with it! 🔗 Check out our project page for the paper & code: princeton-computational-imag… 👏 Thanks to the first authors @Lili_Gao_ , @Yanbo_Xu_ , @WilliamKoc61157

Excited to introduce #TruckDrive 🚛 at #CVPR2026: a new long-range driving dataset built specifically for long-range truck autonomy, where safe braking and anticipatory planning demand perception hundreds of meters ahead, far beyond existing robotaxi datasets. 📦 TruckDrive includes: 🔹 475K samples, with 165K densely annotated frames 🔹 Benchmarks for end-to-end driving, tracking, planning, depth estimation, and up to 1,000m for 2D detection and 400m for 3D detection 📏🎯 🛰️ A purpose-built long-range sensor suite: 🔸 7 long-range FMCW LiDARs (range + radial velocity) 🔸 3 high-res short-range LiDARs 🔸 11× 8MP surround cameras for short and long-range📷 🔸 10× 4D FMCW radars 📡 ⚠️ Key finding: current state-of-the-art models break down at long range 📉 with 31% to 99% drops on 3D perception tasks beyond 150m. TruckDrive exposes a long-range generalization gap that current architectures and training signals are not closing yet - a benchmark for the next generation of long-range highway autonomy research 🚚 🔗 Project and Data: torc-ai.github.io/TruckDrive… Fun work together with @torc_robotics led by Filippo Ghilotti, Edoardo Palladin, Samuel Brucker, Adam Sigal, and Mario Bijelic.

Are we done with object detection? What about tiny objects beyond 200 meters? 🔎 Telescope 🔭 addresses long-range perception by explicitly tackling extreme scale imbalance ⚖️ in images. It hinges on a learnable hyperbolic foveation transform from a low-resolution image, magnifying distant regions 🔍 while compressing nearby ones - effectively normalizing object scales with minimal computational overhead. Objects are detected in the transformed (Riemannian) space using a novel bounding box parameterization and are then mapped back to the original image. Project: light.princeton.edu/telescop…

ScenarioControl 🚗🛣️ - Scenario Generation from a single Dashcam Image 📸 or Text Prompt 💬!! Excited to introduce a new vision-language control mechanism for learned driving scenario generation. Given a single dashcam image or a scene prompt or an image, we generate a full scene layout 🧩, temporally consistent rollouts, including map 🗺️, agents 🚗, and ego video🛣️ ScenarioControl enables direct, fine-grained control over layout and traffic while preserving realism. It operates in a vectorized latent space with a new cross-global control mechanism to fuse vision-language inputs with scene structure while preserving realism. Interfaces seamlessly with generative video models! Project: light.princeton.edu/Scenario… Super fun project by Lili Gao, @Yanbo_Xu_ , William Koch, Samuele Ruffino, @Luke22R , Behdad Chalaki, Dmitriy Rivkin, Julian Ost, @rogg1111, Mario Bijelic.

Chop the gradients ✂️! We found that truncating decoder gradients in latent video diffusion to a fixed window allows us to finetune on videos with pixel-wise perceptual losses without running out of memory. Pixel losses have been essential for image generation and reconstruction, but until now, they haven't scaled to long-duration, high-resolution video diffusion due to recursive activation accumulation in causal decoders, leading to OOM during training 💥📉. Project: light.princeton.edu/chopgrad… Video diffusion models can do a lot more 🚀 when you can backprop the decoder! Post-process neural rendered scenes, super-resolve videos, harmonize lighting in controlled synthetic driving scenes, and inpaint videos — all in a single step ⚡ with a quick finetune from a standard diffusion model.

WorldFlow3D: Unbounded 3D World Generation 🌍 by Flow Through Hierarchical Distributions, without VAEs ! We reformulate 3D generation as flowing through sequentially finer 3D distributions, cutting training time by more than half ⏱️ compared to existing approaches! Vectorized map layouts provide full scene controllability 🗺️, and a novel flow-field alignment process enables causally coherent, spatially unbounded generation 🌍. This generative method generalizes across both real and synthetic data distributions! Project: light.princeton.edu/worldflo… Project led by @amogh7joshi and Julian Ost — will be super fun to build on this! 🔥