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siggraph-related-work

Use when writing or auditing the related-work positioning of a SIGGRAPH / TOG paper, covering the computer-graphics literature lanes (rendering, geo…

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技能内容

SIGGRAPH Related Work

At SIGGRAPH, related work is where you convince a domain-expert reviewer that your contribution is

new capability, not a rediscovery. Graphics reviewers know the canon and the current

state-of-the-art in your sub-area intimately; a missed or mischaracterized prior method is a fast

reject. Position by capability delta — what prior methods cannot do that yours can — not by

chronology. Anchor venue facts to resources/official-source-map.md.

Know the lanes

SIGGRAPH's scope spans distinct sub-communities, each with its own canon and its own reviewers.

Place your paper in the right lane(s) and cover that lane's recent state-of-the-art:

| Lane | What the lane cares about | Adjacent venues to check |

|---|---|---|

| Rendering / light transport | Noise, bias, convergence, speed, physical accuracy | EGSR, HPG, TOG |

| Geometry processing | Robustness on real meshes, guarantees, generality | SGP, TOG |

| Animation / character | Naturalness, control, temporal coherence | SCA, TOG |

| Physical simulation | Stability, energy behavior, time-step, scale | SCA, TOG |

| Imaging / computational photography | Reconstruction quality, artifacts, hardware | ICCP, TOG |

| Geometry/appearance capture, fabrication | Fidelity to real objects, manufacturability | TOG, EG |

| Learning for graphics (neural rendering, generative 3D) | Quality, generality, controllability | CVPR/ICCV overlap, TOG |

| Interaction / VR-AR-MR, HCI-for-graphics | Latency, presence, usability | CHI/UIST overlap, TOG |

A paper often sits in two lanes (e.g., neural rendering = rendering + learning). Cover both; a

reviewer from either lane will check that their canon is represented.

Capability-delta positioning

For each closest prior method, state the axis on which you differ and by how much:

  • Quality: "Prior method X leaves visible noise below N samples; ours converges at N/4."
  • Speed: "X runs offline; ours is real-time at the same quality."
  • Generality: "X assumes manifold input; ours handles the non-manifold meshes practitioners

actually have."

  • Robustness: "X diverges on stiff configurations; ours remains stable."

Never leave the comparison at "we are related to X." Name the axis, and back it with a comparison

in the Results (see siggraph-experiments). The strongest baseline must appear both here and in a

head-to-head figure/table.

Attribution discipline (venue collisions are common)

Graphics ideas migrate across venues; attribute precisely:

  • SIGGRAPH vs SIGGRAPH Asia vs TOG-direct. All three publish in ACM TOG. Cite the specific

issue; do not say "SIGGRAPH" for a SIGGRAPH Asia or a rolling TOG paper.

  • SIGGRAPH vs Eurographics (EG/CGF). Many canonical techniques debuted at Eurographics or in

Computer Graphics Forum, not SIGGRAPH — check dblp before attributing a method to SIGGRAPH.

  • Specialized venues. EGSR (rendering), SGP (geometry), SCA (animation/simulation), HPG

(high-performance graphics), I3D (interactive 3D), ICCP (computational photography) host founding

papers routinely misattributed to SIGGRAPH.

  • The vision overlap. Neural rendering / generative-3D work has a large CVPR/ICCV literature;

cite it, and be clear which contribution is the graphics advance versus the vision one.

Self-positioning and blinding

SIGGRAPH review has historically been single-blind (authors visible), so citing your own prior

work in the natural voice is usually fine — but confirm the current cycle's policy (待核实

for 2026). If a cycle requires anonymized review, cite your prior work in the third person and

avoid "our previous system X."

Anti-patterns

  • A chronological literature tour with no capability delta.
  • Omitting the single strongest baseline because it is inconvenient — the reviewer knows it exists.
  • Attributing an Eurographics/EGSR/SCA method to SIGGRAPH (or vice versa) without checking dblp.
  • Treating a CVPR neural-rendering line as if it did not exist because "this is a graphics venue."
  • Positioning against a strawman instead of the current state-of-the-art in the lane.

Output format

[Lanes] which sub-community/-ies; is each lane's SOTA covered? yes/no
[Strongest baseline] named here and compared head-to-head in Results? yes/no
[Deltas] <prior method -> axis (quality/speed/generality/robustness) -> magnitude>
[Attribution] SIGGRAPH vs SA vs TOG vs EG/EGSR/SGP/SCA checked on dblp? yes/no
[Blinding] cycle policy confirmed; self-cite voice correct? yes/no
[Fixes] <ordered>

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