CamPVG: Camera-Controlled Panoramic Video Generation with Epipolar-Aware Diffusion

arxiv: 2509.19979 · v2 · submitted 2025-09-24 · 💻 cs.CV

CamPVG: Camera-Controlled Panoramic Video Generation with Epipolar-Aware Diffusion

Chenhao Ji , Chaohui Yu , Junyao Gao , Fan Wang , Cairong Zhao This is my paper

Pith reviewed 2026-05-18 14:31 UTC · model grok-4.3

classification 💻 cs.CV

keywords panoramic video generationcamera pose controldiffusion modelsepipolar geometryspherical projectionPlücker embeddingequirectangular projection

0 comments p. Extension

The pith

A diffusion model generates panoramic videos from exact camera pose sequences by encoding poses in spherical coordinates and masking attention along epipolar lines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to extend camera-controlled video generation beyond perspective views to full panoramic output, where standard pose encodings break down on equirectangular images. It introduces CamPVG, a diffusion framework that first encodes camera extrinsics as a panoramic Plücker embedding via spherical coordinate transformation. A spherical epipolar module then applies adaptive attention masking along epipolar lines to aggregate cross-view features while enforcing geometric consistency. If successful, this produces videos whose content stays aligned with the supplied camera trajectory across the full 360-degree sphere. A sympathetic reader cares because accurate panoramic video control would support immersive content creation without manual stitching or post-correction.

Core claim

CamPVG is the first diffusion-based framework for panoramic video generation guided by precise camera poses. It achieves this through camera position encoding for panoramic images and cross-view feature aggregation based on spherical projection, using a panoramic Plücker embedding that captures geometry via spherical coordinate transformation and a spherical epipolar module that enforces constraints through adaptive attention masking along epipolar lines.

What carries the argument

Panoramic Plücker embedding for encoding camera extrinsics via spherical coordinate transformation, paired with a spherical epipolar module that performs adaptive attention masking along epipolar lines on the sphere.

If this is right

Produces panoramic videos whose content remains consistent with supplied camera trajectories
Achieves finer cross-view feature aggregation than prior perspective-only methods
Overcomes the pose-representation failures of standard Plücker embeddings on equirectangular projections
Delivers higher visual quality and geometric fidelity than existing panoramic generation baselines

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same spherical masking pattern could be reused for other 360-degree synthesis tasks such as novel-view synthesis from panoramas
Integrating the pose encoder with real-time camera tracking hardware would enable live panoramic video synthesis
The approach suggests a general template for adapting epipolar constraints to any non-perspective camera model

Load-bearing premise

The assumption that a panoramic Plücker embedding combined with adaptive attention masking along spherical epipolar lines will enforce geometric consistency in the diffusion sampling process without introducing new artifacts or view inconsistencies.

What would settle it

Generate videos from a sequence of camera poses that include rapid rotation followed by translation; visible feature misalignment or stretching across overlapping spherical regions in the output would disprove the claim.

Figures

Figures reproduced from arXiv: 2509.19979 by Cairong Zhao, Chaohui Yu, Chenhao Ji, Fan Wang, Junyao Gao.

**Figure 1.** Figure 1: CamPVG is the first camera-controlled panoramic video generation framework. Given a specified camera trajectory and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Framework of CamPVG. CamPVG employs spherical projection to transform input camera trajectories into panoramic [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Panoramic Plücker Embedding and Epipolar Geometry. Left: transformation from pixel coordinates to Panoramic [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Visualization Results of Spherical Epipolar Atten [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Qualitative Comparison with Baseline Methods. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Qualitative Ablation Study on Different Model Components. Removing any component degrades performance, while [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: More Generated Results of CamPVG [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

read the original abstract

Recently, camera-controlled video generation has seen rapid development, offering more precise control over video generation. However, existing methods predominantly focus on camera control in perspective projection video generation, while geometrically consistent panoramic video generation remains challenging. This limitation is primarily due to the inherent complexities in panoramic pose representation and spherical projection. To address this issue, we propose CamPVG, the first diffusion-based framework for panoramic video generation guided by precise camera poses. We achieve camera position encoding for panoramic images and cross-view feature aggregation based on spherical projection. Specifically, we propose a panoramic Pl\"ucker embedding that encodes camera extrinsic parameters through spherical coordinate transformation. This pose encoder effectively captures panoramic geometry, overcoming the limitations of traditional methods when applied to equirectangular projections. Additionally, we introduce a spherical epipolar module that enforces geometric constraints through adaptive attention masking along epipolar lines. This module enables fine-grained cross-view feature aggregation, substantially enhancing the quality and consistency of generated panoramic videos. Extensive experiments demonstrate that our method generates high-quality panoramic videos consistent with camera trajectories, far surpassing existing methods in panoramic video generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CamPVG is the first diffusion setup for camera-pose-guided panoramic video, using spherical Plücker embeddings and epipolar attention masking, but the consistency gains rest on a soft mechanism whose strength is not yet clear from the numbers.

read the letter

Colleague, the core contribution is a diffusion model that takes camera trajectories and produces panoramic video instead of the usual perspective clips. They convert Plücker coordinates through spherical transformation so the pose encoding fits equirectangular images, then add an attention module that masks along spherical epipolar lines to pull features across views. That combination is new relative to the perspective-only camera-control papers they cite, and it directly tackles the projection mismatch that breaks standard methods on 360 content. The write-up explains the geometric motivation cleanly and shows why direct reuse of existing pose encoders falls short. On the positive side, the architectural choices feel like targeted fixes rather than generic additions, and the task itself matters for VR and simulation work where full-sphere output is needed. The soft spot is the enforcement story. Attention masking inside the denoising loop is a soft operation; nothing in the description shows it overrides the stochastic prior on long sequences or quick motion the way an explicit 3D loss or reprojection term might. The abstract claims substantially better consistency and superior results, yet the summary gives no tables, no ablation isolating the module, and no metrics such as epipolar error or view-consistency scores. If the full experiments contain those controls and the gains hold up, the claim strengthens; without them the link between the module and the output quality stays plausible but unproven. This is aimed at people already working on 360 video synthesis or immersive generation rather than the broader CV community. A reader who needs camera control outside perspective projection will find the ideas worth testing. It has enough novelty and a concrete problem to deserve referee time rather than a desk reject.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes CamPVG, the first diffusion-based framework for panoramic video generation guided by precise camera poses. It introduces a panoramic Plücker embedding that encodes camera extrinsic parameters via spherical coordinate transformation to handle equirectangular projections, along with a spherical epipolar module that performs adaptive attention masking along epipolar lines for cross-view feature aggregation and geometric consistency.

Significance. If the proposed embedding and masking mechanism demonstrably enforce view-consistent geometry in stochastic diffusion outputs, the work would constitute a meaningful extension of camera-controlled generation into the panoramic domain, where pose representation and spherical projection have been longstanding obstacles. This could support downstream applications in immersive media and VR content creation.

major comments (2)

[Abstract] Abstract: the central claim that the spherical epipolar module 'substantially enhancing the quality and consistency' rests on an unverified assumption that adaptive attention masking along epipolar lines will impose sufficiently hard geometric constraints during iterative denoising. Diffusion sampling is noise-driven and can override soft masking, particularly for long sequences or rapid camera motion, yet no auxiliary consistency loss, explicit 3D supervision, or quantitative metric (e.g., epipolar error, 3D reprojection accuracy) is referenced to isolate the module's contribution.
[Abstract] Abstract: the assertion that the method 'far surpasses existing methods' is presented without any reported quantitative results, ablation tables, or baseline comparisons in the provided text. This makes it impossible to evaluate whether the panoramic Plücker embedding and epipolar module deliver measurable gains over prior perspective or panoramic approaches.

minor comments (1)

[Abstract] Abstract: the description of 'camera position encoding for panoramic images' would benefit from a brief clarification of how the spherical coordinate transformation differs from standard Plücker embeddings in perspective settings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below and indicate planned revisions to strengthen the presentation of our claims.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the spherical epipolar module 'substantially enhancing the quality and consistency' rests on an unverified assumption that adaptive attention masking along epipolar lines will impose sufficiently hard geometric constraints during iterative denoising. Diffusion sampling is noise-driven and can override soft masking, particularly for long sequences or rapid camera motion, yet no auxiliary consistency loss, explicit 3D supervision, or quantitative metric (e.g., epipolar error, 3D reprojection accuracy) is referenced to isolate the module's contribution.

Authors: We acknowledge the referee's concern regarding the strength of the claim in the abstract. The full manuscript reports extensive experiments (Section 4) with qualitative results and consistency evaluations that support the module's contribution. To more rigorously isolate the spherical epipolar module's effect and address potential limitations of soft masking in diffusion sampling, we will add quantitative ablations using metrics such as epipolar error and 3D reprojection accuracy in the revised version. revision: yes
Referee: [Abstract] Abstract: the assertion that the method 'far surpasses existing methods' is presented without any reported quantitative results, ablation tables, or baseline comparisons in the provided text. This makes it impossible to evaluate whether the panoramic Plücker embedding and epipolar module deliver measurable gains over prior perspective or panoramic approaches.

Authors: The abstract summarizes findings from the experiments section, which includes quantitative comparisons, ablation studies, and baseline evaluations against prior methods. To improve clarity and avoid ambiguity when the abstract is read in isolation, we will revise the abstract wording to reference the specific experimental results and tables more explicitly. revision: partial

Circularity Check

0 steps flagged

No circularity detected in the derivation chain

full rationale

The paper introduces CamPVG as a new diffusion-based framework featuring a panoramic Plücker embedding for camera pose encoding and a spherical epipolar module for cross-view feature aggregation. No equations, fitted parameters, or self-citations are presented in the abstract or described claims that reduce any prediction or result to its own inputs by construction. The method is framed as an architectural innovation addressing panoramic-specific challenges, with consistency claims tied to the proposed modules rather than re-derivations or renamed prior results. This qualifies as a self-contained proposal without load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the effectiveness of two newly introduced geometric modules whose independent validation is not provided in the abstract; standard diffusion assumptions are inherited from prior work.

axioms (1)

domain assumption Spherical projection and epipolar geometry can be directly adapted to diffusion attention without breaking the generative prior learned on perspective data.
Invoked when the paper states that the panoramic Plücker embedding and spherical epipolar module enable fine-grained cross-view aggregation.

invented entities (2)

panoramic Plücker embedding no independent evidence
purpose: Encode camera extrinsic parameters for equirectangular panoramic images via spherical coordinate transformation.
New encoding introduced to overcome limitations of traditional Plücker embeddings on panoramic projections.
spherical epipolar module no independent evidence
purpose: Enforce geometric constraints via adaptive attention masking along epipolar lines in spherical space.
New attention mechanism proposed to improve cross-view consistency in generated panoramic videos.

pith-pipeline@v0.9.0 · 5732 in / 1372 out tokens · 37606 ms · 2026-05-18T14:31:26.371873+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

we propose a panoramic Plücker embedding that encodes camera extrinsic parameters through spherical coordinate transformation... spherical epipolar module that enforces geometric constraints through adaptive attention masking along epipolar lines
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

SphericEpiAttn(𝑞𝑖, 𝑘, 𝑣)=softmax(𝑞𝑖𝑘⊤/√𝑑 ⊙M 𝑖)𝑣

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages · 3 internal anchors

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Imagine360: Immersive 360 Video Generation from Perspective Anchor.CoRR abs/2412.03552 (2024). doi:10.48550/ARXIV.2412.03552 arXiv:2412.03552 Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kaifeng Lyu, and Kai Chen. 2025. LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?arXiv preprint arXiv:2503.1...

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CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

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