[Paper] Predicting Camera Pose from Perspective Descriptions for Spatial Reasoning

Source: arXiv - 2602.06041v1

Overview

The paper tackles a core limitation of today’s multimodal large language models (MLLMs): reasoning about a scene from multiple camera angles. By explicitly modeling camera pose, the authors enable a system to “take perspective” – i.e., to understand a 3D environment from several 2‑D images and answer questions from a new, language‑specified viewpoint. The result is a fast, pose‑aware framework (CAMCUE) that dramatically improves accuracy while cutting inference time from minutes to seconds.

Key Contributions

• CAMCUE framework – a pose‑aware multi‑image architecture that injects per‑view camera pose into visual tokens and fuses them across viewpoints.
• Natural‑language pose grounding – a module that translates free‑form viewpoint descriptions (e.g., “standing on the left side of the table”) into concrete camera pose parameters (rotation + translation).
• Imagined target view synthesis – generates a pose‑conditioned “mental image” of the scene from the queried viewpoint to feed downstream reasoning modules.
• CAMCUE‑DATA – a curated dataset of 27,668 training and 508 test instances containing multi‑view images, exact camera poses, and diverse natural‑language viewpoint descriptions, including human‑annotated test queries.
• Efficiency gains – eliminates the costly test‑time search‑and‑match pipeline, reducing per‑example inference from ~256 s to ~1.5 s.

Methodology

1. Pose‑augmented visual encoding – each input image is processed by a vision encoder (e.g., ViT). The associated 6‑DoF camera pose (3‑D rotation + translation) is embedded and added to the visual token embeddings, giving the model a geometric anchor for each view.
2. Cross‑view fusion – a transformer‑based fusion layer aggregates the pose‑aware tokens from all source images, allowing the model to build a unified 3‑D representation of the scene.
3. Language‑to‑pose grounding – a lightweight language model parses the natural‑language description of the target viewpoint and predicts the corresponding pose vector. This step replaces brute‑force pose search used in prior work.
4. Target‑view imagination – using the predicted pose, a conditional image synthesis module (e.g., a diffusion model) renders a “mental” view of the scene from that perspective.
5. Answer generation – the imagined view and the fused scene representation are fed to a multimodal LLM that produces the final answer to spatial‑reasoning questions (e.g., “What is behind the red chair from the new viewpoint?”).

All components are trained end‑to‑end on CAMCUE‑DATA, with supervision on both pose prediction (rotation/translation loss) and QA accuracy.

Results & Findings

• The model reliably translates free‑form viewpoint language into accurate pose estimates (>90 % within tight error bounds).
• By synthesizing the imagined view, CAMCUE achieves a sizable boost in spatial‑reasoning accuracy over pose‑agnostic baselines.
• The speedup makes interactive applications (e.g., AR assistants) feasible.

Practical Implications

• AR/VR content creation – developers can feed a handful of captured images and a textual description (“view from the balcony”) to instantly generate a coherent novel view, accelerating scene authoring.
• Robotics & navigation – a robot equipped with a camera can understand commands like “look at the object from the opposite side of the hallway” without exhaustive pose enumeration, enabling faster planning.
• 3‑D reconstruction pipelines – CAMCUE’s pose grounding can serve as a lightweight alternative to traditional Structure‑from‑Motion when only sparse views and natural language cues are available.
• Interactive AI assistants – chat‑based agents can answer “what does the room look like from the kitchen window?” in real time, opening new UX possibilities for smart home dashboards.

Limitations & Future Work

• Dataset bias – CAMCUE‑DATA is synthetic‑heavy; performance on highly cluttered, real‑world indoor scenes may degrade.
• Pose granularity – The current pose predictor outputs a single 6‑DoF estimate; handling ambiguous or multi‑modal viewpoint descriptions (e.g., “somewhere near the door”) remains open.
• Scalability of view synthesis – While inference is fast, the imagined view generation still relies on a diffusion model that can be memory‑intensive for high‑resolution outputs.
• Extension to dynamic scenes – The framework assumes static environments; incorporating temporal cues for moving objects is a promising direction.

Bottom line: By marrying explicit geometry with language, CAMCUE demonstrates that multimodal models can reason across viewpoints efficiently—a step toward truly spatially aware AI systems that developers can plug into AR, robotics, and interactive applications today.

Authors

• Xuejun Zhang
• Aditi Tiwari
• Zhenhailong Wang
• Heng Ji

Paper Information

• arXiv ID: 2602.06041v1
• Categories: cs.CV
• Published: February 5, 2026
• PDF: Download PDF